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<p>Applied Energy 114 (2014) 798–808</p><p>Contents lists available at ScienceDirect</p><p>Applied Energy</p><p>journal homepage: www.elsevier .com/ locate/apenergy</p><p>A case study for biogas generation from covered anaerobic ponds</p><p>treating abattoir wastewater: Investigation of pond performance</p><p>and potential biogas production</p><p>0306-2619/$ - see front matter Crown Copyright � 2013 Published by Elsevier Ltd. All rights reserved.</p><p>http://dx.doi.org/10.1016/j.apenergy.2013.10.020</p><p>⇑ Corresponding author. Tel.: +61 07 46 311 623; fax: +61 07 46 311 530.</p><p>E-mail addresses: Bernadette.McCabe@usq.edu.au (B.K. McCabe), Ihsan.</p><p>Hamawand@usq.edu.au (I. Hamawand), Peter.Harris@usq.edu.au (P. Harris), Craig.</p><p>Baillie@usq.edu.au (C. Baillie), Talal.Yusaf@usq.edu.au (T. Yusaf).</p><p>Bernadette K. McCabe ⇑, Ihsan Hamawand, Peter Harris, Craig Baillie, Talal Yusaf</p><p>National Centre for Engineering in Agriculture, University of Southern Queensland, Toowoomba, QLD, Australia</p><p>h i g h l i g h t s</p><p>�We report on the performance of a novel covered anaerobic pond system.</p><p>� Potential biogas production was estimated using BioWin modelling software.</p><p>� Ponds maintained stable operation; however, accumulation of crust was an issue.</p><p>� Modelling indicated that biogas yield can be influenced by decomposition efficiency.</p><p>� Configuration and operation of ponds can also impact potential biogas production.</p><p>a r t i c l e i n f o</p><p>Article history:</p><p>Received 10 May 2013</p><p>Received in revised form 7 October 2013</p><p>Accepted 9 October 2013</p><p>Available online 30 October 2013</p><p>Keywords:</p><p>Anaerobic digestion</p><p>Wastewater</p><p>Biogas</p><p>Modelling</p><p>BioWin</p><p>Slaughterhouse</p><p>a b s t r a c t</p><p>Covered anaerobic ponds offer significant advantages to the red meat processing industry by capturing</p><p>methane rich gas as a fuel source for bioenergy while reducing greenhouse gas emissions (GHG). This</p><p>paper presents the results of a novel-designed anaerobic pond system at an Australian abattoir in relation</p><p>to pond performance and potential biogas production. Key findings in assessing the effectiveness of the</p><p>system revealed that the covered ponds are capable of efficient wastewater decomposition and biogas</p><p>production. The primary issue with the covered ponds at the abattoir was the build-up of fat/crust that</p><p>prevented the accurate measurement of biogas and effective use of the cover. In the absence of field bio-</p><p>gas data the novel application of the computer modelling software BioWin� was carried out to simulate</p><p>chemical oxygen demand (COD) removal rates and subsequent biogas yield. The unique parameter used</p><p>to fit field data was the fraction of the inlet COD due to a superficial crust which did not follow anaerobic</p><p>digestion. Field data effluent COD removal rates were matched to simulated rates predicted by BioWin</p><p>when measured influent COD was reduced to 30%. Biogas modelling results suggest significant variation</p><p>in the economic benefit of biogas energy, with the quantity of biogas potentially varying tenfold (from</p><p>328 m3/d to 3284 m3/d) depending on site factors such as pond efficiency, pond configuration and oper-</p><p>ational practices.</p><p>Crown Copyright � 2013 Published by Elsevier Ltd. All rights reserved.</p><p>1. Introduction</p><p>Anaerobic waste treatment ponds are widely adopted in the</p><p>meat industry as the first stage of secondary treatment of high-</p><p>strength abattoir wastewater and are an efficient means whereby</p><p>the biochemical oxygen demand (BOD) and chemical oxygen de-</p><p>mand (COD) are reduced by around 90% during ideal conditions</p><p>[1]. They are the preferred option for treating agricultural waste-</p><p>water in Australia due to their relatively low initial cost, negligible</p><p>operating costs and simplicity of operation [2]. However, they have</p><p>a couple of issues including odour emissions and the generation of</p><p>methane, a powerful greenhouse gas (GHG). The Australian red</p><p>meat processing industry has a high exposure to carbon pricing</p><p>due to wastewater methane emissions and its use of coal for steam</p><p>generation [3]. Consequently, the industry is beginning to install</p><p>covered anaerobic pond technology [4]. Despite higher initial infra-</p><p>structure costs when compared to uncovered anaerobic ponds,</p><p>covered anaerobic ponds offer significant advantages such as odour</p><p>control, intensification of the decomposition process and BOD re-</p><p>moval, an increase in feed rate and the potential for capturing</p><p>methane-rich gas as a fuel source for bioenergy and the reduction</p><p>in GHGs [4–6]. Energy obtained from the biogas can be used in an</p><p>internal combustion engine coupled to an electric generator to</p><p>http://crossmark.crossref.org/dialog/?doi=10.1016/j.apenergy.2013.10.020&domain=pdf</p><p>http://dx.doi.org/10.1016/j.apenergy.2013.10.020</p><p>mailto:Bernadette.McCabe@usq.edu.au</p><p>mailto:Ihsan.Hamawand@usq.edu.au</p><p>mailto:Ihsan.Hamawand@usq.edu.au</p><p>mailto:Peter.Harris@usq.edu.au</p><p>mailto:Craig.Baillie@usq.edu.au</p><p>mailto:Craig.Baillie@usq.edu.au</p><p>mailto:Talal.Yusaf@usq.edu.au</p><p>http://dx.doi.org/10.1016/j.apenergy.2013.10.020</p><p>http://www.sciencedirect.com/science/journal/03062619</p><p>http://www.elsevier.com/locate/apenergy</p><p>Nomenclature</p><p>ARE absolute relative error</p><p>BOD biochemical oxygen demand</p><p>COD chemical oxygen demand</p><p>DAF dissolved air flotation</p><p>EC electrical conductivity</p><p>FOG fats, oils and greases</p><p>GHG greenhouse gas</p><p>HDPE high density polyethylene</p><p>HRT hydraulic retention times</p><p>ML mega litre</p><p>OLR organic loading rates</p><p>ORP oxidation-reduction potential</p><p>SRT solid retention time</p><p>TA total alkalinity</p><p>tHSCW tonnes of hot standard carcass weight</p><p>TKN total Kjeldahl nitrogen</p><p>TSS total suspended solids</p><p>VFA volatile fatty acid</p><p>B.K. McCabe et al. / Applied Energy 114 (2014) 798–808 799</p><p>produce electrical power, or simply be used in boilers. Also, the li-</p><p>quid fraction from this process can be used as liquid fertilizer [7].</p><p>Knowledge regarding the design of these ponds and the quan-</p><p>tity and quality of biogas captured remains largely undetermined.</p><p>Modelling has previously been used to predict biogas production</p><p>after calibration. In a study by Martinez et al. [8] modelling was</p><p>used to simulate slaughterhouse effluent waste degradation and</p><p>methane generation after it was demonstrated that the model</p><p>showed an accurate reproduction of the behaviour of an anaerobic</p><p>digester. Modelling the biogas production process can be used as</p><p>an indication of the process performance. This may identify actions</p><p>for better control of the process operation that positively impact</p><p>the biogas yield [9].</p><p>This paper firstly provides contextual background information</p><p>and proceeds to report on the performance of a novel designed</p><p>covered anaerobic pond system installed at an Australian abattoir</p><p>in relation to wastewater treatment and biogas production. The</p><p>study also reports on the novel application of BioWin� computer</p><p>modelling software to simulate biogas yield in a field-based situa-</p><p>tion and provides an economic assessment of biogas recovery and</p><p>use based on these modelled results.</p><p>2. Overview of operation and performance of anaerobic ponds</p><p>treating abattoir effluent</p><p>Red meat processing produces wastewater with a high pollu-</p><p>tant load consisting of paunch, manure, fats, oils and greases</p><p>(FOGs), and uncollected blood. These components contribute to a</p><p>high-strength waste which must be treated to reduce the BOD,</p><p>COD, FOGs and total suspended solids (TSS) [10]. FOGs are large</p><p>contributors to BOD and COD and while FOGs have the potential</p><p>to produce large quantities of methane [11], their recalcitrant nat-</p><p>ure generally results in a number of problems [12]. Some of the</p><p>problems include: clogging of pipes; foul odour generation; adhe-</p><p>sion to the bacterial cell surface and reducing their ability to treat</p><p>wastewater; and flotation of sludge and loss of active sludge</p><p>[13,14]. FOGs also tend to accumulate on the surface of ponds to</p><p>form a recalcitrant scum layer or ‘crust’ [15,16] which can hamper</p><p>attempts to accurately measure biogas. However, primary treat-</p><p>ment systems such as dissolved air flotation (DAF) units with</p><p>chemical treatment are capable of reducing FOGs by up to 89–</p><p>98% [5].</p><p>Anaerobic digestion is said to be</p><p>working optimally when the</p><p>acid formation phase (hydrolysis and acidogenesis) and the meth-</p><p>ane production phase (acetogenesis and methanogenesis) occur</p><p>simultaneously in dynamic equilibrium [17]. Stability of the</p><p>anaerobic process is difficult to maintain because a balance</p><p>favourable to several microbial populations is necessary and the</p><p>comparatively stable nature of the acid formers and the fastidious</p><p>nature of the methane formers creates a biosystem that is prone</p><p>to upset as a result of shock loads or temperature fluctuations</p><p>[18]. Therefore, for the design of an anaerobic pond to perform</p><p>optimally, it must be based on the limiting characteristics of</p><p>these microorganisms. Pond oxidation–reduction potential</p><p>(ORP), temperature, NH3 concentration, pH, volatile fatty acid</p><p>(VFA) to total alkalinity (TA) ratios (VFA/TA) are all parameters</p><p>which are indicative of pond performance and should be moni-</p><p>tored [1]. However, criteria for anaerobic pond design are poorly</p><p>defined and no widely accepted overall design equation exists</p><p>[19]. Previously, pond construction criteria for the red meat pro-</p><p>cessing industry has been derived from other industries, and this</p><p>has resulted in pond designs which have not necessarily been</p><p>suitable. Design is typically based on organic loading rates</p><p>(OLR) and hydraulic retention times (HRT) from pilot plants and</p><p>observations of existing pond systems [19]. Generally, the desired</p><p>goal is to achieve significant reductions in wastewater organic</p><p>load with the least HRT possible [15]. Anaerobic ponds are de-</p><p>signed based on an OLR to promote sedimentation of wastewater</p><p>solids and efficient anaerobic digestion to biogas. Compared with</p><p>anaerobic digesters, anaerobic ponds are designed for relatively</p><p>low OLRs [20]. Overloading of ponds has the undesirable effect</p><p>of accumulating inhibitory substances which inhibit biogas pro-</p><p>duction and reduce biogas yield. As a general rule, an increase</p><p>in organic loading must be balanced by an increase in HRT to</p><p>achieve equivalent treatment efficiency of the wastewater [21].</p><p>There is currently a lack of knowledge within the Australian</p><p>red meat processing industry regarding the design and operation</p><p>of anaerobic ponds and upgrading these to covered anaerobic</p><p>ponds to minimise GHG from wastewater treatment operations.</p><p>Also, there is a clear lack of published literature which details</p><p>biogas production using this technology with the recoverable</p><p>quantity and quality of biogas remaining largely unclear. In the</p><p>absence of meaningful field gas measurements it is difficult to as-</p><p>sess the feasibility of using covered anaerobic ponds to generate</p><p>bioenergy. Computer modelling software has become widely</p><p>adopted in wastewater engineering over the past two decades.</p><p>Although evolved principally as a research tool they are now used</p><p>more for design and optimisation of wastewater treatment plants</p><p>[22]. BioWin is a Windows based computer simulation model</p><p>which is increasingly used to predict anaerobic digestion pro-</p><p>cesses and subsequent biogas yield [23]. It is used primarily to</p><p>simulate wastewater treatment for domestic sewage and there</p><p>has been no application to meat processing waste to date. How-</p><p>ever, anaerobic ponds that treat abattoir effluent utilise the same</p><p>complex microbiological processes responsible for the anaerobic</p><p>decomposition of domestic wastewater. Thus there is great scope</p><p>to apply the tool in this situation given the uncertainty surround-</p><p>ing accurate biogas measurements due to crust and solid</p><p>accumulation.</p><p>800 B.K. McCabe et al. / Applied Energy 114 (2014) 798–808</p><p>3. The case study of Churchill Abattoir: novel pond design and</p><p>cover construction</p><p>Churchill Abattoir Pty Ltd is a medium-sized meat processing</p><p>facility located in South East Queensland, Australia. The abattoir</p><p>slaughters and processes around 3000 head of cattle per week</p><p>resulting in around 660 tonnes of hot standard carcass weight</p><p>(tHSCW) per week.</p><p>In 2009 the abattoir started to investigate the use of covered</p><p>anaerobic ponds to reduce offensive odours and greenhouse gas</p><p>emissions with the subsequent capture of methane for bioenergy.</p><p>This prompted a re-design of the wastewater treatment system.</p><p>McCabe et al. [24] provides a detailed background of the novel</p><p>pond design and cover construction. Briefly, 5 smaller anaerobic</p><p>ponds were constructed, each 50 m in length, 20 m in width and</p><p>5 m in depth, with an effective volume of 2.2 ML each. This design</p><p>was selected for two main reasons, namely manageability for des-</p><p>ludging ponds and ease of removing and applying covers. A new</p><p>floating cover design was proposed whereby covers were attached</p><p>to a floating raft or truss which holds the cover off the surface of</p><p>the pond. HDPE pipe (100 mm) was used to form the skeleton of</p><p>the raft and these pipes were filled with expansive foam to stiffen</p><p>the structure and aid in floatation and HDPE mat was used as the</p><p>cover material.</p><p>4. Methods</p><p>4.1. Monitoring schedule and wastewater characterisation</p><p>Fig. 1 illustrates a schematic of the 5 pond layout. A total of</p><p>43 weeks of sampling was performed during two stages on ponds</p><p>A, B and E. Stage one consisted of 19 weeks and stage two</p><p>24 weeks. Sampling was conducted twice-weekly at the com-</p><p>mencement of the sampling campaign for 9 weeks and then</p><p>weekly thereafter. The monitoring schedule provided in Table 1</p><p>lists the parameters that were measured as part of the monitoring</p><p>protocol. Both on-site and laboratory analysis was conducted.</p><p>Wastewater samples for laboratory analysis were collected and</p><p>analysed by Australian Laboratory Services (ALS) group (Brisbane,</p><p>sample ports</p><p>Wastewater from plant</p><p>Save-all</p><p>Anaerobic pond A Anaerobic pond B</p><p>Anaerobic pond C Anaerobic pond D</p><p>Anaerobic pond E Facultative anaerobic pond 2</p><p>Aerobic pond 3</p><p>Irrigated to crops</p><p>Fig. 1. Schematic of pond layout indicating sampling points and flow of</p><p>wastewater.</p><p>Australia). Measured parameters included COD, BOD, TSS, FOG,</p><p>ammonia as nitrogen (NH3–N), total Kjeldahl nitrogen (TKN), alka-</p><p>linity, and volatile fatty acids (VFA). On-site wastewater analysis</p><p>involved the measurement of wastewater temperature, pH, EC</p><p>and ORP using a YSI professional plus field logger. Biogas was ana-</p><p>lysed for methane, carbon dioxide, oxygen, and hydrogen sulphide</p><p>content, as well as the remaining balance using a Geotechnical</p><p>instruments GA2000 landfill gas analyser which is capable of mea-</p><p>suring methane, carbon dioxide, hydrogen sulphide and oxygen to</p><p>within 98%.</p><p>Fixed ultrasonic flow meters (Dalian Zerogo RV-100F) were at-</p><p>tached to the external surface of the inflow pipes to ponds A and B.</p><p>Flow meter data was logged at a frequency of one minute and</p><p>stored on a CR1000 data logger. Sampling ports were installed as</p><p>shown in Fig. 1 and included inlets to pond A and B, and the outlets</p><p>of ponds A, B and E. Ponds A and B were the primary focus of mon-</p><p>itoring since these serve as the two primary ponds receiving all</p><p>incoming wastewater. These two ponds run in parallel and feed</p><p>into a further series of three anaerobic ponds; C, D and E. Ponds</p><p>C, D and E function as a single unit with bidirectional flow between</p><p>ponds, with flow direction dependent on pond level, although flow</p><p>is generally unidirectional C ? D ? E ? pond 2. The outflow of</p><p>Pond E was also monitored to further understand the operation</p><p>of the novel anaerobic pond system as a whole.</p><p>4.2. Biogas simulation</p><p>Due to the difficulties encountered in measuring biogas produc-</p><p>tion at the site, dynamic wastewater treatment modelling using</p><p>the software BioWin (EnviroSim Associated LTD, Canada) was</p><p>undertaken to estimate biogas production. BioWin is a Microsoft</p><p>Windows-based simulator which is used in the analysis and design</p><p>of wastewater treatment plants. BioWin uses a general Activated</p><p>Sludge/Anaerobic Digestion (ASDM) model which is referred to as</p><p>the BioWin General Model. BioWin is interface software which re-</p><p>quires input data to carry out the simulation. Parameters such as</p><p>flow rate, total COD, TKN, total P, total N, pH, alkalinity, inorganic</p><p>S.S., Ca, Mg, and DO are main characteristics of the wastewater re-</p><p>quired by BioWin. These values (either constant or variable with</p><p>time) are presented in a hypothetical setting which then re-creates</p><p>the anaerobic digestion process by the software. To enhance the</p><p>prediction of the software, the wastewater fractions such as readily</p><p>biodegradable, non-colloidal slowly biodegradable, unbiodegrad-</p><p>able soluble and particulate are requested. Moreover, process ki-</p><p>netic parameters such as hydrolysis rate with stoichiometric</p><p>parameters are essential for high accuracy predictions. Although,</p><p>kinetic and stoichiometric parameters are important input data</p><p>for the software, BioWin includes default values for these parame-</p><p>ters which have previously been found reliable in this study [25].</p><p>BioWin contains two operational modules which include a steady</p><p>state module and an interactive dynamic simulator. The steady</p><p>state module is used for simulating systems based on constant</p><p>conditions while the dynamic simulator allows the user to change</p><p>time varying inputs or changes in operational strategy which</p><p>Table 1</p><p>Sampling history for ponds A, B and E.</p><p>Pond Effluent Number</p><p>of</p><p>samples</p><p>Parameters</p><p>A (uncovered) Inflow and</p><p>outflow</p><p>16 TSS, alkalinity, NH3–N,</p><p>TKN, FOG, COD, BOD, VFA.</p><p>pH, EC, ORP, temperatureB (covered) Inflow and</p><p>outflow</p><p>40</p><p>E (uncovered) Outflow 17</p><p>Fig. 2. Anaerobic pond configuration at Churchill Abattoir.</p><p>B.K. McCabe et al. / Applied Energy 114 (2014) 798–808 801</p><p>reflect real conditions. Thus, dynamic modelling using BioWin was</p><p>deemed an appropriate tool for simulating the behaviour of the</p><p>covered anaerobic ponds in this study.</p><p>To simulate the anaerobic ponds at Churchill Abattoir, BioWin</p><p>was first calibrated against measured data from the field monitor-</p><p>ing programme. Data sets used in the calibration process included</p><p>effluent COD concentration and TSS concentration. The calibration</p><p>process was conducted using a relatively complete and parallel</p><p>data set for ponds B and E over 150 days. Data for Pond B was used</p><p>to test the skill of BioWin simulating a unit process while data for</p><p>pond E was used to test the skill in modelling the whole wastewa-</p><p>ter system. Fig. 2 shows the configuration of the 5 ponds at Chur-</p><p>chill Abattoir as represented by BioWin interface window.</p><p>5. Results and discussion</p><p>5.1. Decomposition efficiency</p><p>The average flow data into ponds A and B is given in Tables 2</p><p>and 3. The average OLR for ponds A and B was 2.3 kgCOD m�3 d�1</p><p>and 3.4 kgCOD m�3 d�1 respectively with an average HRT of be-</p><p>tween 2 and 3 days. CSIRO [4] provides a recommended OLR of</p><p>Table 2</p><p>Removal efficiencies of the five pond system during stage 1 sampling.</p><p>Parameter Number of samples Average</p><p>Pond A</p><p>Flow rate (m3/d) 79,200 503.29</p><p>Influent COD (mg/L) 15 7442.00</p><p>Effluent COD (mg/L) 23 2885.30</p><p>Influent BOD (mg/L) 15 3402.67</p><p>Effluent BOD (mg/L) 24 1318.39</p><p>Influent TSS (mg/L) 15 3235.00</p><p>Effluent TSS (mg/L) 23 1496.09</p><p>Influent FOG (mg/L) 15 491.87</p><p>Effluent FOG (mg/L) 24 111.30</p><p>Pond B</p><p>Flow rate (m3/d)a 142,560 658.44</p><p>Influent COD (mg/L) 27 7051</p><p>Effluent COD (mg/L) 27 2696.30</p><p>Influent BOD (mg/L) 27 3273.04</p><p>Effluent BOD (mg/L) 27 852.26</p><p>Influent TSS (mg/L) 27 2990.63</p><p>Effluent TSS (mg/L) 27 1196.15</p><p>Influent FOG (mg/L) 27 618.74</p><p>Effluent FOG (mg/L) 27 95.85</p><p>Pond E (Five-pond system)</p><p>Effluent COD (mg/L) 10 1155.20</p><p>Effluent BOD (mg/L) 10 188.80</p><p>Effluent TSS (mg/L) 10 704.10</p><p>Effluent FOG (mg/L) 10 29.40</p><p>a % Total reduction for five-pond system.</p><p>0.05–0.08 kgCOD m�3 d�1 with a HRT of 20–40 days. Both BOD</p><p>and COD loading rates are outside these recommended operating</p><p>parameters which are expected given the short HRT for each of</p><p>the ponds. If, however, the 5 ponds are considered as an integrated</p><p>waste treatment system, ponds C, D and E are operating within de-</p><p>sign parameters. While the system appears to be operating within</p><p>design parameters there are components which are operating out-</p><p>side the criteria. This has obvious implications for the maintenance</p><p>of the pond system, particularly in regard to solids management.</p><p>During the stage one sampling period (Table 2) pond A achieved</p><p>73% COD removal while pond B achieved a lesser removal of 53%.</p><p>The total% COD removal of the 5-pond system was 84% based on</p><p>the outflow of pond E. The lower COD removal of pond B reflects</p><p>the OLR of this pond which was calculated at an average of</p><p>2.275 kgCOD m3 d, which was approximately double that of pond</p><p>A during the same time period (1.03 kgCOD m3 d). The COD re-</p><p>moval efficiency of pond B was not detrimentally affected when</p><p>pond A was taken off line for desludging at the end of stage one</p><p>monitoring. The higher OLR of pond B during the second sampling</p><p>period did not result in a corresponding decrease in solids removal</p><p>efficiency with the% COD removal maintained at 59% (Table 3). A</p><p>similar trend exists for BOD removal for the 3 ponds over the same</p><p>two sampling periods.</p><p>Standard deviation Range Av% reduction</p><p>11.65 10.04–899.38</p><p>2678.12 2630–12,100</p><p>2220.68 798–9150 73.22</p><p>1109.87 1410–5150</p><p>1203.00 188–4610 74.95</p><p>1353.16 1370–6830</p><p>1568.08 292–5640 76.25</p><p>259.52 73–962</p><p>816.54 <5–4080 85.26</p><p>15.78 15.16–1207.68</p><p>2895.10 1040–12,100</p><p>870.97 1680–4710 53.47</p><p>1461.68 163–7020</p><p>184.13 575–1500 62.19</p><p>1573.18 457–6870</p><p>755.13 567–4020 39.79</p><p>509.83 5–2110</p><p>89.34 21–520 89.25</p><p>–a</p><p>265.98 672–1660 83.62</p><p>67.49 78–302 94.23</p><p>421.36 138–1700 76.46</p><p>26.3 8–98 95.25</p><p>Table 3</p><p>Removal efficiencies of the five pond system during stage 2 sampling.</p><p>Parameter Number of samples Average Standard deviation range Av% reduction</p><p>Pond B</p><p>Flow rate (m3/d)a 89,280 1019.30 820.04 21.57–3028.04</p><p>Influent COD (mg/L) 13 9216.15 5978.34 4330–24,200</p><p>Effluent COD (mg/L) 13 2898.92 1024.00 836–5020 58.89</p><p>Influent BOD (mg/L) 13 5087.69 6131.70 1060–24,500</p><p>Effluent BOD (mg/L) 13 714.62 436.91 246–1920 73.49</p><p>Influent TSS (mg/L) 13 3874.62 1533.58 1760–6130</p><p>Effluent TSS (mg/L) 13 1988.77 1292.53 824–5360 35.11</p><p>Influent FOG (mg/L) 13 1388.23 1310.21 136–4570</p><p>Effluent FOG (mg/L) 13 91.54 38.95 23–167 83.39</p><p>Pond E (Five-pond system) –a</p><p>Effluent COD (mg/L) 7 900.07 618.74 126–2150 72.94</p><p>Effluent BOD (mg/L) 7 88.79 33.33 5–130 77.67</p><p>Effluent TSS (mg/L) 7 413.64 220.47 210–867 77.89</p><p>Effluent FOG (mg/L) 7 27.71 45.75 49–143 91.98</p><p>a % Reduction for five-pond system.</p><p>802 B.K. McCabe et al. / Applied Energy 114 (2014) 798–808</p><p>It was observed that both COD and BOD removal efficiencies</p><p>(particularly the latter) decreased over the two sampling periods</p><p>for the 5-pond system owing to the accumulation of crust and</p><p>sludge over this time. The COD and BOD of outflow samples of</p><p>pond E at the end of the second sampling period are 73% and</p><p>78% respectively. This compares to the earlier efficiencies of COD</p><p>and BOD removal of 84% and 94% at the end of the first sampling</p><p>period.</p><p>The suspended solids removal was more efficient for pond A</p><p>then pond B with 76% and 40% recorded respectively over the first</p><p>stage of monitoring. This probably contributed to the increase in</p><p>sludge build up that occurred in pond A leading to its subsequent</p><p>desludging at the end of its first 18 months of operation. The over-</p><p>all TSS removal of pond B was low over both stage one and stage</p><p>two monitoring periods and may indicate that the short HRT did</p><p>not permit adequate sedimentation of wastewater solids.</p><p>The removal of FOGS by the 5-pond system is 95% during stage</p><p>one (pond E data, Table 2) and is generally maintained throughout</p><p>stage two at 92% (pond E, Table 3). The increase in OLR for pond B</p><p>during the second stage of monitoring marginally decreased the</p><p>FOG removal efficiency from 89% to 83% and this would have con-</p><p>tributed to the slight decrease in FOGs removal efficiency of the</p><p>whole system at this time.</p><p>Figs. 3a and b shows the fat accumulated on uncovered pond A</p><p>and covered pond B respectively. Both ponds A and B accumulated</p><p>approximately 1 m of crust over the 2 year operation since these</p><p>two ponds received the majority of organic load. This crust accu-</p><p>mulation meant that a reduction in the effective volume of the</p><p>Fig. 3a. Appearance</p><p>of crust accumulation on uncovered pond A.</p><p>Fig. 3b. HDPE cover on pond B. Note the presence of the thick crust.</p><p>pond occurred over time which could impact on the bioconversion</p><p>efficiency of the two ponds.</p><p>5.2. Biogas quality</p><p>The quality of biogas based on the constituents methane (CH4),</p><p>carbon dioxide (CO2), oxygen (O2) and hydrogen sulphide (H2S)</p><p>produced from covered pond B are shown in Figs. 4a and b. It is</p><p>important to note that a spike in OLR occurred during April having</p><p>the effect of lowering CH4 and CO2 at this time. The levels of CH4</p><p>and CO2 returned to nominal levels after the shock loading event.</p><p>Fig. 4. Pond B biogas major constituents (a) and minor constituents (b) during stage 1 and 2 sampling period.</p><p>B.K. McCabe et al. / Applied Energy 114 (2014) 798–808 803</p><p>Average CH4 content was 52%, while CO2 and O2 were 22% and 3%</p><p>respectively. The levels of O2 should be negligible; however, the</p><p>cover did become compromised at various stages of the monitoring</p><p>and did not achieve an air tight seal. Average H2S levels over the</p><p>same period were 686 ppm. To compare field results 3 samples</p><p>were sent to analytical labs (SGS, Sydney, Australia). These results</p><p>show that the CH4 values ranged from 59% to 62% with CO2 and O2</p><p>levels averaging 37% and 0.9% respectively. H2S levels ranged be-</p><p>tween 47 and 196 ppm.</p><p>5.3. Wastewater simulation</p><p>Wastewater decomposition efficiency was simulated using Bio-</p><p>Win by implementing a step-wise reduction in influent COD and</p><p>adjusting this to best match the measured effluent COD. The influ-</p><p>ent from the covered primary anaerobic pond (pond B) was used</p><p>for modelling purposes. In order to justify the reduction of the</p><p>COD input into BioWin, a sensitivity analysis was conducted for</p><p>the stoichiometric and the kinetic parameters of the software. It</p><p>was found that altering the parameters in BioWin did not improve</p><p>the results between simulated and measured data when the model</p><p>was operating under no COD reduction. Modified input COD data</p><p>established that a 30% reduction of influent COD was the best fit</p><p>for the model. In practice this means only 30% of the influent</p><p>COD was taking part in the anaerobic digestion process. The</p><p>remaining 70% of the COD can be accounted for through the accu-</p><p>mulation of fat and other undigested material (such as paunch) at</p><p>the top of the pond and undigested sludge at the bottom which</p><p>was consistent with observations at the site.</p><p>This first part of the modelling process demonstrated that Bio-</p><p>Win was able to accurately simulate a single anaerobic pond de-</p><p>spite the severe fluctuation in both the inflow rate and influent</p><p>water composition. Two methods were used to show the agree-</p><p>ment between the measured and predicted data. Two trends sim-</p><p>ilar to the predicted data were plotted. They represent a value of</p><p>20% above and below the predicted data and represents the agree-</p><p>ment between the predicted and measured data enclosed by ±20%</p><p>of the predicted data values. Absolute relative error (average)</p><p>(ARE) was also used to show the agreement between the measured</p><p>and predicted data. The equation below was used to estimate the</p><p>ARE [26];</p><p>ARE ¼ 1</p><p>N</p><p>�</p><p>XN</p><p>i¼1</p><p>jðmi � piÞj</p><p>mi</p><p>� 100%</p><p>where mi is the measured value of the output variable, pi predicted</p><p>value of the output variable and N number of the observations. Due</p><p>to the high complexity of the process, and as stated by other</p><p>researchers [26], an average relative error for the measured and</p><p>predicted data of 7–15% is sufficient for indication of correct dy-</p><p>namic calibration. In Liwarska’s case [26], it is worth mentioning</p><p>that monitored data was fitted to the simulated values over a period</p><p>of a few hours. In the current study, monitored data was fitted with</p><p>predicted BioWin data over a three month period, resulting in abso-</p><p>lute relative errors of between 14% and 21%. The slightly higher er-</p><p>ror in comparison to Liwarska’s case was attributed to high</p><p>fluctuation in the characteristic of the wastewater, the environment</p><p>condition around the ponds, thick crust formation and the long per-</p><p>iod of sampling. In light of these conditions it is fair to suggest that</p><p>an average error of 21% is quite reasonable.</p><p>The BioWin prediction of COD effluent from pond B is shown in</p><p>Fig. 5a. Predicted and measured COD results were graphed against</p><p>each other where the absolute relative error was found to be 14%.</p><p>This was considered to be very good, particularly when considering</p><p>the high fluctuation of the flow rate and varying composition of</p><p>influent to the pond. BioWin simulations were also able to demon-</p><p>strate similar skill with data collected at different dates and pond</p><p>temperatures as shown in Fig. 5b. In addition to COD BioWin was</p><p>able to show skill in simulating the effluent TSS. There was good</p><p>agreement between measured TSS and BioWin prediction at two</p><p>sampling period with an absolute relative error of 18% as shown</p><p>in Figs. 6a and b.</p><p>The next stage of the modelling process focused on the ability of</p><p>BioWin to simulate the 5-pond system. The 5-pond system was</p><p>configured in BioWin and measured effluent COD from Pond E</p><p>(which represents the final outflow) was plotted against BioWin</p><p>predictions of COD. As shown in Fig. 7a the measured COD of the</p><p>outlet wastewater from Pond E again correlates very well with</p><p>the BioWin predictions with an absolute relative error value of</p><p>Fig. 5. Measured and simulated effluent COD from Pond B for (a) stage 1 and (b)</p><p>stage 2 monitoring periods. Fig. 6. Measured and simulated effluent TSS from Pond B for (a) stage 1 and (b)</p><p>stage 2 monitoring periods.</p><p>804 B.K. McCabe et al. / Applied Energy 114 (2014) 798–808</p><p>16%. In addition to the simulations of COD, TSS measured at the</p><p>outlet of pond E was also compared against the data predicted by</p><p>BioWin and is shown in Fig. 7b. Simulation of TSS gave an absolute</p><p>relative error value of 21%. This analysis and interpretation demon-</p><p>strates clearly the ability of BioWin to simulate both a single ele-</p><p>ment and the whole system of wastewater treatment at Churchill</p><p>Abattoir.</p><p>Further validation of the model is provided by comparing mea-</p><p>sured biogas quality (% methane) obtained from pond B with pre-</p><p>dicted BioWin results over a period of approximately 3 months.</p><p>Predicted and measured values were graphed against each other</p><p>where the absolute relative error was found to be 17% (Fig. 8). This</p><p>level of correlation gives further support for the validity of the soft-</p><p>ware in predicting biogas quality and provides additional confi-</p><p>dence in predicting pond efficiency.</p><p>5.4. Biogas production potential</p><p>Potential biogas production was estimated by simulating the</p><p>anaerobic processes within the ponds over 364 days to represent</p><p>annual biogas production. Measured data from the monitoring pro-</p><p>gram including flow rates and COD were used as inputs into the</p><p>BioWin simulations. To assess biogas production for the current</p><p>system and operation practices, two scenarios were modelled. Sce-</p><p>nario 1 represents a COD reduction efficiency of 85% (default set-</p><p>tings within BioWin) while scenario 2 represents a COD</p><p>reduction efficiency of 30%.</p><p>The data contained in Table 4 is a summary of the modelled re-</p><p>sults for potential annual production of biogas under the current</p><p>Fig. 7. Measured and simulated effluent COD (a) and TSS (b) from Pond E during</p><p>stage 1 monitoring period.</p><p>B.K. McCabe et al. / Applied Energy 114 (2014) 798–808 805</p><p>configuration (refer to Fig. 2) and management of ponds at the</p><p>abattoir under an ideal circumstance (i.e. scenario 1) where the</p><p>efficiency of the pond is high and governed by 85% COD reduction</p><p>(default within BioWin). The data contained in Table 4 includes</p><p>minimum, maximum and average biogas production during this</p><p>period. Total annual biogas production of 431,404 m3 was found</p><p>by summing the simulated daily gas production of the 5-pond</p><p>system.</p><p>The calibration of the model however indicated that the ponds</p><p>are likely converting only 30% of the influent COD via anaerobic</p><p>digestion. The annual gas production at Churchill Abattoir is there-</p><p>fore most</p><p>likely to be similar to the data presented in Table 5 (i.e.</p><p>scenario 2) which is based on a 30% reduction in COD resulting in a</p><p>significantly lower annual biogas yield of 120,000 m3 (equivalent</p><p>to 0.0298 m3/m3/d). This compares with the study conducted by</p><p>Safley and Westerman [27] which measured 0.03–0.5 m3/m3/d</p><p>and is illustrative of the large range of biogas quantities which</p><p>can be produced by anaerobic ponds.</p><p>Modelling suggested other factors, despite ideal digestion (80–</p><p>90% COD reduction) are likely to significantly affect the process.</p><p>These include the HRT, solid retention time (SRT), temperature</p><p>and flow rate. The current design of the ponds through the model-</p><p>ling process was assessed to be operating at 30–40% efficiency</p><p>when combining these factors. It is important to note that this rep-</p><p>resents the final production yield of the ponds. It is reasonable to</p><p>expect that over the lifetime of the ponds the biogas production</p><p>yield will initially be much higher due to a greater useable volume</p><p>of the pond (i.e. before crust and sludge accumulation). The final</p><p>production yield of the pond could in fact be enhanced through</p><p>the routine removal of crust and sludge throughout the lifetime</p><p>of the ponds.</p><p>5.5. Alternative configurations and operational options</p><p>Previous lab based studies by Borja et al. [28] suggest that in-</p><p>creased biogas production can be achieved when the variables</p><p>affecting the performance of the anaerobic process (i.e. ponds)</p><p>are better controlled. To examine these possibilities and by exploit-</p><p>ing the functionality of BioWin, alternative configurations and</p><p>operational options were simulated to identify the impact on bio-</p><p>gas yield. As an example, a new configuration is shown in Fig. 9</p><p>which includes the addition of a clarifier to recycle the activated</p><p>sludge leaving the system. Simulation modelling of this system</p><p>by BioWin did show a significant improvement in the performance</p><p>of the ponds by increasing biogas yield.</p><p>Table 6 presents the biogas production from an alternative pond</p><p>configuration (scenario 3) where most of the inlet COD is consid-</p><p>ered as degradable materials (ideal scenario presented earlier).</p><p>The potential biogas production is around 3284 m3/d. Even by</p><p>reducing the amount of degradable COD to 30% (likely scenario</p><p>at Churchill presented earlier) the results shown in Table 7 (sce-</p><p>nario 4) demonstrates significant improvement in biogas produc-</p><p>tion (i.e. 572 m3/d from 328 m3/d). These figures indicate the</p><p>potential to significantly increase biogas production by a relatively</p><p>minor change in the configuration of the treatment system. In this</p><p>instance the last pond at Churchill (pond E) could be used as a clar-</p><p>ifier pond to recycle the activated sludge back into the top of the</p><p>system.</p><p>5.6. Cost analysis</p><p>Biogas has an energy content of (6.0–7.5) KW h/m3 which is</p><p>comparable to coal seam gas (9.9 KW h/m3), making it a very</p><p>important source of energy [29]. One of the most likely options</p><p>for biogas capture and use at Churchill Abattoir is via a combined</p><p>heat and power generation plant to offset electricity and heating</p><p>demands at the site. Based on the BioWin modelling results the</p><p>amount of biogas produced from the site is 120,000 m3/year</p><p>(328 m3/d). Each cubic metre of biogas contains the equivalent of</p><p>6 kW h or 21.6 MJ of energy. However, when biogas is converted</p><p>to electricity, via a biogas powered electric generator, approxi-</p><p>mately 35% of the total energy is converted to electricity due to</p><p>the efficiency of the generator. The remainder of the energy is con-</p><p>verted into heat, some of which can be recovered for heating appli-</p><p>cations. It is assumed that 35% of the total energy can also be</p><p>recovered for low grade heating purposes [30].</p><p>5.6.1. Energy offsets</p><p>Table 8 presents the amount of useable energy for the site</p><p>produced from biogas based on the assumptions described above.</p><p>Fig. 8. Measured methane content in biogas vs. BioWin prediction, ARE 17%.</p><p>Table 4</p><p>Scenario 1 – Total and individual Biogas production from the ponds at Churchill plant</p><p>(Ideal: 85% efficiency).</p><p>Pond Biogas</p><p>production</p><p>(m3/year)</p><p>Production (m3/d)</p><p>Min Max Average</p><p>Pond A 130,639 119 556 362</p><p>Pond B 136,821 37 742 380</p><p>Pond C 58,264 61 228 161</p><p>Pond D 52,604 50 212 146</p><p>Pond E 48,344 49 184 134</p><p>Total biogas production m3per year</p><p>(Five-pond system)</p><p>431,404 1183</p><p>Table 5</p><p>Scenario 2 – Total and individual Biogas production from the ponds at the Churchill</p><p>plant (30% efficiency).</p><p>Pond Biogas</p><p>production</p><p>(m3/year)</p><p>Production (m3/d)</p><p>Min Max Average</p><p>Pond A 48,881 59 103 94</p><p>Pond B 54,822 24 142 111</p><p>Pond C 21,200 58 79 49</p><p>Pond D 19,552 18 71 45</p><p>Pond E 14,671 4 90 29</p><p>Total biogas production m3per year</p><p>(Five-pond system)</p><p>120,000 328</p><p>Fig. 9. Alternative ponds’ configu</p><p>806 B.K. McCabe et al. / Applied Energy 114 (2014) 798–808</p><p>Scenario (1) highlights the likely biogas production from the cur-</p><p>rent operation of the ponds (described earlier) and the opportunity</p><p>to exploit biogas based energy on the site. Energy savings based on</p><p>other BioWin modelling scenarios are also presented in Table 8.</p><p>Assuming a conservative electricity price of $0.1/kW h, electric-</p><p>ity costs on site can be offset by $25,200 per annum. Given a coal</p><p>price of $88/tonne, the total cost of coal is offset by $2,957 per an-</p><p>num due to the recoverable heat energy from the biogas power</p><p>generation process. Combined, the total energy costs at Churchill</p><p>can be offset by $28,157 (scenario 1) under current operating con-</p><p>ditions. It is important to note however that the potential is much</p><p>greater depending on the operational configuration and perfor-</p><p>mance of the ponds. Based on the other BioWin modelling results,</p><p>energy costs could be offset by $49,040 by changing the operating</p><p>configuration of the ponds at the current efficiency (scenario 2).</p><p>5.6.2. Economic assessment of biogas recovery and use</p><p>A rudimentary economic analysis was undertaken to assess the</p><p>feasibility of biogas energy recovery and use at Churchill and for</p><p>the scenarios described above. The economic assessment was</p><p>based on a simple payback period (SPP) approach for a combined</p><p>heat and power generation plant. The assessment was based on</p><p>the following assumptions:</p><p>� The capital cost of the generation equipment plus</p><p>additional costs including design, planning and project</p><p>management is $1,200/kW.</p><p>ration at Churchill Abattoir.</p><p>Table 6</p><p>Scenario 3 – Total and individual Biogas production from the ponds at the Churchill</p><p>plant (ideal: 85% efficiency; alternate configuration).</p><p>Pond Biogas</p><p>production</p><p>(m3/year)</p><p>Production (m3/d)</p><p>Min Max Average</p><p>Pond A 311,510 198 1516 855</p><p>Pond B 375,046 544 1630 1030</p><p>Pond C 267,285 435 1091 734</p><p>Pond D 242,162 383 1007 665</p><p>Pond E</p><p>Total biogas production m3per year</p><p>(Five-pond system)</p><p>1,209,139 3284</p><p>Table 7</p><p>Scenario 4 – Total and individual biogas production from the ponds at the Churchill</p><p>plant (30% efficiency; alternate configuration).</p><p>Pond Biogas</p><p>production</p><p>(m3/year)</p><p>Production (m3/d)</p><p>Min Max Average</p><p>Pond A 66,952 216 270 184</p><p>Pond B 64,169 128 302 176</p><p>Pond C 40,849 49 278 112</p><p>Pond D 36,115 42 246 100</p><p>Pond E</p><p>Total biogas production m3per year</p><p>(Five-pond system)</p><p>209,000 572</p><p>Table 8</p><p>Energy saving at Churchill Abattoir plant.</p><p>Scenario Biogas</p><p>(m3/year)</p><p>Useable</p><p>energy</p><p>from</p><p>biogas</p><p>Energy</p><p>amt.</p><p>(GJ/</p><p>year)</p><p>Energy</p><p>amt.</p><p>(kW h)</p><p>Energy</p><p>savings</p><p>($)</p><p>Energy</p><p>offset</p><p>1 431,404 Electricity 3261 905,948 $90,595 Electricity</p><p>heat 3261 905,948 $10,630 coal</p><p>2 120,000 Electricity 907 252,000 $25,200 Electricity</p><p>heat 907 252,000 $2957 coal</p><p>3 1,209,139 Electricity 9141 2,539,192 $253,919 Electricity</p><p>heat 9141 2,539,192 $29,793 coal</p><p>4 209,000 Electricity 1580 438,900 $43,890 Electricity</p><p>heat 1580 438,900 $5150 coal</p><p>B.K. McCabe et al. / Applied Energy 114 (2014) 798–808 807</p><p>� The generator required is based on 100 kW per 40 m3/h of</p><p>biogas.</p><p>� Other lifetime costs for Operation and Maintenance (O&M)</p><p>is half of the initial capital cost.</p><p>The results from the SPP analysis are presented in Table 9 which</p><p>indicates a payback on the investment including an allowance</p><p>for</p><p>life time O&M costs of 2.2 years. As a general guide and for this</p><p>exercise an investment with a payback of less than 3 years is con-</p><p>sidered to be an attractive proposition for the Meat Processing</p><p>Industry (pers. comm. Mike Spence June 2012). Given the analysis</p><p>is based on proportional costs and returns relative to the quantity</p><p>of biogas produced the SPP for both scenarios are the same. The</p><p>Table 9</p><p>Simple payback period (SPP) based on the investment in construction of a combined heat</p><p>Scenario Biogas (m3/h) Power generator size (kW) Capital cost</p><p>1 49 123 $147,875</p><p>2 14 34 $41,000</p><p>3 137 342 $410,500</p><p>4 24 60 $71,500</p><p>financial proposition however will be significantly different over</p><p>the lifetime of the investment for each scenario and requires a</p><p>more detailed analysis.</p><p>6. Conclusions</p><p>The purpose of this study was to gauge covered anaerobic per-</p><p>formance in terms of both waste treatment efficiency and subse-</p><p>quent biogas production. Observations from this work indicate</p><p>that the successful design and operation of the covered anaerobic</p><p>ponds is highly sensitive to the inclusion of FOGs in the effluent</p><p>stream entering the ponds. This problem is not unique to Churchill</p><p>Abattoir and is a systemic problem in the Australian red meat pro-</p><p>cessing industry which hinders the successful uptake of technolo-</p><p>gies such as covered anaerobic ponds.</p><p>This study reports on the novel application of computer model-</p><p>ling using BioWin software to simulate COD removal rates and</p><p>subsequent biogas yield. The application of wastewater modelling</p><p>using BioWin in this study has provided some initial insights into</p><p>how unbiodegradable portions of COD can affect predicted waste-</p><p>water treatment and subsequent biogas yield. Due to the high</p><p>strength nature of abattoir wastewater, the accumulation of crusts</p><p>on anaerobic ponds can result in limited ability to accurately ob-</p><p>tain biogas measurements. The simulated results provide an initial</p><p>indication that BioWin may be a useful tool in determining biogas</p><p>yield in complex systems where it is difficult to obtain accurate</p><p>data. Once calibrated, BioWin was found to closely predict mea-</p><p>sured data, despite the severe fluctuation in both inlet flow and</p><p>water quality parameters. In this instance BioWin was able to sim-</p><p>ulate the behaviour of the anaerobic ponds and simulated an aver-</p><p>age biogas yield of 328 m3/d. The modelling has shown that the</p><p>total energy cost at Churchill Abattoir can be offset by $28,157 un-</p><p>der current operating conditions. This includes electricity costs of</p><p>$25,200 and cost of coal of $2,957 per annum. Modelling also sug-</p><p>gests this can be significantly increased (by a factor of ten) with</p><p>relatively minor changes to the system configuration and</p><p>operation.</p><p>In terms of industry benefits BioWin has the ability to be a use-</p><p>ful tool in the analysis of pond performance, that is, varying the de-</p><p>fault parameters in BioWin has the potential to determine how</p><p>efficient the anaerobic pond is operating. For example, reducing</p><p>the hydrolysis rate’s default value in order to match the measured</p><p>data with BioWin prediction is an indication of low efficiency of</p><p>the anaerobic pond. This can be related to the pond design, low</p><p>degradability of the influent waste, and/or to microbiological as-</p><p>pects. The actual cause can then be determined via further simula-</p><p>tion through altering other related parameters to aid in optimising</p><p>the amount of potential biogas produced.</p><p>Acknowledgements</p><p>The work described in this article was fully supported by the</p><p>Australian Meat Processor Corporation (AMPC) and Meat and Live-</p><p>stock Australia (MLA). Support given by Mike Spence (Company</p><p>Engineer, Churchill Abattoir) is gratefully acknowledged.</p><p>and power generation plant.</p><p>($) O&M ($) Total costs ($) Offset ($) SPP (years)</p><p>$73,938 $221,813 $101,225 2.2</p><p>$20,500 $61,500 $28,157 2.2</p><p>$205,250 $615,750 $283,712 2.2</p><p>$35,750 $107,250 $49,040 2.2</p><p>808 B.K. McCabe et al. / Applied Energy 114 (2014) 798–808</p><p>References</p><p>[1] Mittal GS. Treatment of wastewater from abattoirs before land application—a</p><p>review. Bioresource Technol 2006;97(9):1119–35.</p><p>[2] Laginestra M, van-Oorschot. 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