A SPREADSHEET TOOL TO CALIBRATE LANDGEM TM GAS MODELING PREDICTION SOFTWARE FOR SITE SPECIFIC MSW FACILITIES USING DATA FROM GAS EXTRACTION AUDITS

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1 A SPREADSHEET TOOL TO CALIBRATE LANDGEM TM GAS MODELING PREDICTION SOFTWARE FOR SITE SPECIFIC MSW FACILITIES USING DATA FROM GAS EXTRACTION AUDITS C.J. CRONIN*, P. KELLY*, T.RUDDY *, D.SMYTH *, S.MEYLER* Fehily Timoney & Co. Consultants in Engineering and Environmental Sciences SUMMARY: Accurate gas prediction models are a fundamental requirement if gas extraction in municipal solid waste (MSW) landfill sites is to be effective. Effective gas managment typically requires development of one or more theoretical models and regular calibration of the model(s) thereafter, using frequent on-site audits. Accurate modelling is required to predict gas production in order to: manage environmental impact; facilitate financial modelling; program grid connections, utilisation and flaring infrastructure and other operational considerations. The algorithms developed in this paper facilitate calibration of landfill gas prediction estimates produced using LandGEM TM for any defined year using audit records of gas extraction flow rates and historical waste inputs. 1. INTRODUCTION Flaring and utilisation of landfill gas, carbon reduction targets and carbon credit trading from MSW landfills in developing countries, requires gas management and auditing processes. Prediction modelling and calibration is required to minimise environmental impacts and maximise financial returns. Proprietary software (LandGEM TM and GasSim TM ) or specialised in-house developed gas prediction models are typically used in Europe to facilitate the planning and design of gas collection infrastructure. Validation of alternate model predictions has also been the subject of historical studies (Coops et al., 1997). LandGEM TM version 3.02 was developed by the United States Environmental Protection Agency (Blakey et al., 1992 and Alexander et al., 2005) as an automated gas estimate tool based on empirical data from US landfills. It is a relatively simple tool using a first order organic waste decomposition rate equation with an excel interface. Proceedings Sardinia 2011, Thirteenth International Waste Management and Landfill Symposium S. Margherita di Pula, Cagliari, Italy; 3-7 October by CISA, Environmental Sanitary Engineering Centre, Italy

2 For independent audit purposes there are significant advantages to using LandGEM TM. It has been developed: with database records from arid to wet climates; is readily accessible in spreadsheet format from US based web sites for free; is potentially useful to waste planners world wide; and provides a simple tool to estimate gas production flow rates from MSW landfills Accordingly the spreadsheet calibration tool presented in this paper was designed to support LandGEM TM in order to help landfill managers and planners audit and calibrate prediction model estimates. The spreadsheet calibration tool uses a series of algorithms to develop a locus of landfill gas prediction curves by varying methane generation rate (k) and potential methane generation capacity (Lo) for defined waste inputs. Other algorithms select the closest five prediction curves with reference to audited gas extraction flows from any time period which then allows graphical comparison and selection of the best fit curve against audit results. This closest fit curve can also be compared against gas prediction outputs from other proprietary packages to facilitate planning and financial analysis for future developments. This paper is an enhancement of a flow based gas extraction philosophy (Cronin et al 2008) developed within Ireland to facilitate balancing of landfill gas extraction from MSW sites. Experience gained from development of bespoke gas management software using a flow based balancing philosophy resulted in the development of the spreadsheet algorithms presented in this paper. This paper: will explain the structure behind the calibration tool, and describe potential uses by way of a case study. 2. OVERVIEW OF LANDFILL GAS BALANCING MSW landfills operating under anaerobic conditions produce landfill gas. Methane and carbon dioxide are the predominant constituent gases. Methane is typically oxidised in engines to power electrical generators or in flares to minimise methane emmissions and to manage odours. Gas production from MSW sites is very sensitive to organic and moisture contents within waste. For effective use of any prediction model it is important to calibrate the model using data from regular gas extraction (balancing) site audits in relation to both gas quality and extraction flow rate making due allowance for fugitive (typically surface) emissions and oxygen ingress if over extraction is occurring or if the cap is inadequate. Environmental best practice requires landfill gas to be extracted and either flared, utilised or conditioned at a rate equal to the production rate of the waste body (Cronin et al., 2008).

3 Balancing of landfill gas extraction is typically carried out at intervals of one month or greater and is balanced relative to either methane or oxygen concentrations to facilitate engine and or flare operating criteria. Flows are typically measured only at key manifolds. This paper advocates that records of flow during the audit are taken at all point sources be they individual wells manifolds or similar and representative capture efficiencies applied to reflect capped and uncapped areas. Once an accurate assessment of extraction flow rate and fugitive emissions is determined, following an audit, then it is possible to to generate a best fit curve using the calibration software which can be used to validate or other wise question input parameters used in the LandGEM TM prediction model. 3. OVERVIEW OF LANDGEM TM LandGEM TM estimates emission rates for total landfill gas, methane, carbon dioxide, nonmethane organic compounds (NMOCs), and individual air pollutants from MSW waste streams in the United States using database records from arid to wet climates. LandGEM TM relies on the following variables to estimate landfill gas emissions: methane generation rate (k) which is a function of; - moisture content, - availability of nutrients for microorganisms, - ph of waste, and - temperature. potential methane generation capacity (Lo) which is a function of; - waste type; and - composition the higher cellulose. non methane organic compound concentration (NMOC) which is a function of; - waste type; and - extent of reactions. methane content; - the model assumes 50 % v/v methane and 50% v/v carbon dioxide. - the recommended range of allowable methane concentration is between 40 % and 60 % v/v 4.0 OVERVIEW OF CALIBRATION SOFTWARE Following a landfill gas extraction audit, the audited extraction flow rate is adjusted to accommodate fugitive emissions and gas quality (if required). Thereafter adjusted extraction flow rate observations can be used in the calibration model to validate k and Lo variables used in LandGEM TM to develop theoretical gas flow rate prediction estimates for any defined year.

4 The model assumes that for any given waste type, LandGEM TM will predict a total finite amount of landfill gas and the rate of waste breakdown will influence the time period over which the gas is produced. Therefore the area under respective prediction curves (ie the volume of landfill gas with a methane concentration between 40 % and 60 % v/v) is assumed to be similar. References hereafter notated thus Year refer to range names (in this case Year) within the spreadsheet model. For the defined Year the model then selects the five gas flow prediction curves for respective k and Lo values which are closest to the audit defined Target gas flow rate prediction value. This data is presented graphically for comparison against current and historical audit findings. Selection of the best fit curve can then be reviewed against available gas prediction curves and historical audit findings. Experience in Ireland has shown that even with accurate audit findings over extended time periods, it can be very difficult to calibrate proprietary models with a view to obtaining accurate future gas production estimates. Use of any model or calibration tool therefore needs to used with caution. The primary purpose of this calibration model is to challenge historical gas prediction assumptions in order to facilitate ongoing reviews of planned infrastructure based on evidence from landfill gas prediction audits. 5.0 CALIBRATION MODEL STRUCTURE Table 1 Input model parameters k and Lo for LandGEM TM ver 3.02 Table 2 LandGEM TM prediction of landfill gas generation Table 3 Summary outputs of LandGEM TM for respective k and Lo combinations for a defined Year Table 4 Selection of best fit outputs from LandGEM TM Table 5 Summary of best fit k and Lo variable inputs for LandGEM TM Figure 1 Calibration Model Structure

5 LandGEM TM software (version 3.02) was copied into a dedicated spreadsheet (Fehily Timoney, 2011). Algorithms enter the variables k and Lo and the total landfill gas production estimates for respective variable combinations. The results are collated in a table and presented graphically for a defined Year showing available prediction model estimates and historical audit observations. The following section describes the spreadsheet structure by showing formulas in respective cells. The excel screen illustrations show selected rows and 3 columns only of the model. Text shown in CAPITAL LETTERS refers to specific spreadsheet functions. Table 1 provides the locations for variable model inputs k and Lo used in a TWO VARIABLE DATA INPUT TABLE. Table 1 Model Parameters Table 2 uses a modified copy of LandGEM TM to predict landfill gas generation flow rates by facilitating entry of multiple combinations of k and Lo for a defined Year the outputs of which are presented in Table 3. Table 2 LandGEM TM Gas Predictions Table 3, for the Year and Target flow rate entered by the user, collates outputs from Table 2 Average Total Landfill Gas Emissions (m 3 /hr) prediction estimates using a TWO VARIABLE DATA TABLE INPUT for respective Table 1 combinations. Table 1 k and Lo inputs are entered using the ARRAY function ={TABLE (E3,E4)}.

6 Table 3 Two Variable Data Input Table for k and Lo Table 4 algorithms select the closest five curves to the audited Target gas extraction flows, from Table 3, for a defined Year. Table 4 Selection of Closest Prediction Curves to Audit Findings Row 333 uses ARRAY formulae to define within each Year, the closest ABS flow to the ABS Target flows, eg in Year0 the range selected is Data0, using functions INDEX, MATCH and SMALL. Row 334 defines the difference between the closest MATCH and the Target. Rows 335 through to 339 uses the function SMALL to select in ascending order the five closest LandGEM TM predictions. Rows 340 through 349 define the respective k and Lo variable for the five closest curves.

7 Table 5 presents the variable inputs k and Lo required in LandGEM TM to produce the five gas production estimates which are closest to the Target flows for a defined Year. Table 5 Closest Prediction Curves to Audit Findings 6.0 CASE STUDY Gas balancing audits were carried out on the Arthurstown site in Co Kildare, Ireland in 2008 using a flow based balancing technique (Cronin et al., 2008) with site specific gas balancing and auditing software. The audit results allowed comparison of observed flows from respectives wells against GasSim TM and LandGEM TM predictions for the whole site. The site was licensed to accept 600,000 tonnes per annum of baled MSW waste and produced in excess of 13 MW of electricty during the peak of its landfill gas production. The site was also the only baled landfill site in Ireland. Auditing of the site showed a significant variance between gas prediction model estimates and actual audit observations. The prediction model defaults for the site used in both LandGEM TM and GasSIM TM had to be revised. As a consequence of this model calibration, additional engines were added to maximise extraction of landfill gas and significant additional revenue from power generation resulted. The subjective reason for the discrepancy is believed to have been caused by baling and poor compaction which allowed preferential pathways to develop and so allow rainfall to increase moisture content at depth within the waste body resulting in accelerated waste degradation. Accurate estimates of gas prediction has also allowed long term provision to be made for flaring infrastructure with appropriate turn down ratios and module sizing. Use of the calibrated LandGEM TM prediction curve has also been used to facilitate present value analysis of utilisation options to facilitate decisions on whether or not it was economically viable to invest in engines over defined time periods. 7.0 SUMMARY Balancing of landfill gas extraction experience in Ireland demonstrated the importance of calibrating landfill gas prediction curves on a regular basis following audits and gas balancing exercises to facilitate financing, planning and design of associated infrastructure. To facilitate the calibration exercise, LandGEM TM ver 3.02 was copied and altered such that for a defined year landfill gas prediction estimates for all combinations of k and Lo could be produced.

8 Spreadsheet algorithms then selected the five closest landfill gas volume prediction curves for a defined year and tabulated the k and Lo variable inputs required to produce prediction curves to allow comparison against historical audit findings. The primary purpose of the subject calibration model is to challenge historical gas prediction assumptions in order to facilitate ongoing reviews of planned infrastructure based on evidence from landfill gas prediction audits. REFERENCES Blakey N.C., Cossu R., Maris P.J. and Mosey F.E. (1992). Anaerobic lagoons and UASB reactors: Laboratory experiments. In Landfilling of waste: Leachate, Christensen, Cossu, Stegmann (Eds), Elsevier Applied Science Publisher, Amsterdam, pp (Free download from Alexander A., Burklin C., Singleton A., Landfill Gas Emissions Model (LandGEM) Version 3.02 User s Guide EPA-600/R-05/047 May 2005, (Free download Coops O., Luning L., Oonk H., Weenk A. (1995) Validation of landfill gas formation models, Sardinia 95 5th International Landfill Symposium, October 1997, Cagliari, Italy, volume IV, pp C. J. Cronin, P. Kelly, E. Hanley, T. Ruddy, J. Smith (2008) A Management And Auditing Model For Balancing Landfill Gas Extraction Proceedings Waste 2008: Waste and Resource Management a Shared Responsibility 1 Stratford-upon-Avon, Warwickshire, England, September Golder Associates (UK) Ltd, managing organisation for Waste Fehily Timoney (2011), Calibration Software (Free download from ).