Schaich, Laurel

Laurel Schaich (pron shy) is an environmental engineer with seven years of experience in municipal and industrial bioenergy and biosolids projects....

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About Schaich, Laurel

Laurel Schaich (pron shy) is an environmental engineer with seven years of experience in municipal and industrial bioenergy and biosolids projects. Laurel joined CDM Smith after completing her BS in chemical engineering at University of Nebraska and recently completed her MS in environmental engineering at University of Kansas. Laurel is a classically trained musician she and her husband are a piano/violin freelance duo.

Schaich, Laurel

Titles from this speaker

Monte Carlo Modeling Approach for Anaerobic Digestion and Biogas Utilization

Abstract

Background and Objectives: Methods to recover energy at Water Reclamation Facilities (WRFs) are becoming increasingly studied and implemented. One source of recovered energy from wastewater is biogas generated from anaerobic digestion. This bioenergy is commonly used for digester heating at WRFs in boilers or combined heat and power (CHP) systems. Upgrading biogas to renewable natural gas (RNG) for pipeline injection and use in vehicle fuel is an alternative with additional opportunity due to federal and state incentive programs. To elucidate economic and energy tradeoffs between CHP and RNG systems, a Bioenergy Model was developed to compare biogas utilization in a CHP system versus an RNG system for 2,270 cubic meters per day (500,000 gallons per day) of anaerobic digester feed. The Bioenergy Model accounts for distributions in digester feed, digestion kinetics, utility costs, renewable identification number (RIN) values, and biogas utilization system parameters to evaluate uncertainty and sensitivity of project financials and energy utilization. Methodology: Ten projects evaluating biogas utilization in CHP and/or RNG were used to establish the range of inputs applicable to CHP and RNG systems. The previous EPA analysis informed additional system input assumptions of CHP (2017). CHP and RNG scenarios were modeled to determine equipment sizing and costs, capital costs, operating expenses, and financial benefits. For CHP, financial benefits were evaluated based on power and heat produced that the plant could beneficially use. For RNG, financial benefits included revenue from the wholesale gas and sale of D3 RINs through the Renewable Fuel Standard (RFS). A Monte Carlo analysis with 10,000 simulations was applied to understand the uncertainty of the inputs used in the model, and Spearman's correlation coefficients were determined to assess the sensitivity of outputs to inputs. For each Monte Carlo simulation, anaerobic digestion kinetic parameters outputted biogas flows and energy available. The resulting average biogas production was approximately 1,400 normal cubic meters per hour (Nm3/hr), or 900 standard cubic feet per minute (scfm). Historical D3 RIN values were used for analysis, assuming a constant value for each simulation based on weekly D3 prices since 2015 (Figure 1). Historical RIN Prices are presented, sorted by D-code (D3 and D5). Note that $1 per RIN is equivalent to $0.04 per kWh ($11.727 per MMBtu) (U.S. Environmental Protection Agency, 2021). External economic factors (e.g., future predictions of RIN values) were not incorporated. The model accounted for a range of methane recovery and parasitic load in RNG systems, including membrane systems, liquid scrubbing systems, and pressure swing adsorption. Net present value was determined by discounting project capital expenses for the analysis period (i.e., 20 years). Findings: Results of the analysis show generally positive economic benefits for both CHP and RNG, with median Year 1 values of $3,300,000 for RNG projects and $990,000 for CHP. The CHP net annual values were typically lower than RNG net annual values and had a tighter range. The net value of RNG projects was highly varied due to the range of RIN values ($0.48 to $3.24 per RIN) and the associated RIN revenue. However, despite the high revenue potential of RNG projects, in approximately one-percent of the evaluated scenarios, the net RNG value was negative (i.e., RNG operations costs outweighed the financial benefit). Year 20 values were also determined for CHP and RNG projects and compared to Year 1 values (Figure 2). Net present values for CHP projects and RNG projects are presented for Year 1 and Year 20 of the analysis period. The standard box and whiskers chart from Microsoft Excel was used, which presents the median (as the middle line), the average (as the 'x'), the range from median of the first quartile to median of the third quartile (as the box), local minima and maxima (as the whiskers), and outliers (as dots). Spearman's rank correlation coefficients were determined to evaluate the inputs with the most impact, both positive and negative, on the results. Based on the Spearman's evaluation, parameters with significant impact on project financials included plant power cost ($/kwh), RIN values, and digester feed (Figure 3). Spearman's rank coefficients were determined for Year 1 net values of CHP and RNG projects. The red line represents the Spearman's value correlated to a P value of 0.05, signifying variables of significance. To further understand sensitivity of critical variables, plant power cost and RIN values were further evaluated to compare a ratio of RNG to CHP Year 1 values (Figure 4), with a value of 1 representing equal value between RNG and CHP, values above 1 favoring RNG scenarios, and values below 1 favoring CHP scenarios. Results from the Monte Carlo analysis were plotted in a contour plot and smoothed with a Loess smoothing function (sampling proportion of 0.1, polynomial degree of 1.0). The contour plot is presented as a logarithmic scale, where the Value Ratio is given by: Value Ratio=10^(RNG Value-CHP Value)/|RNG Value|. A value ratio of 1.0 represents that the RNG and CHP values are equal. If the value ratio is greater than 1.0, RNG is more financially viable; likewise, a value ratio of less than 1.0 represents a scenario where CHP is more economical. The black line plotted represents a value ratio of 1.0. In addition to a financial analysis, the Bioenergy Model was also used to compare net energy produced for CHP and RNG scenarios (Figure 5). Net energy from CHP and RNG projects were compared and plotted in box and whisker plots. Net energy is expressed as MMBtu/day (1 MMBtu/day = 12.2 kW) and as a percentage of the inlet biogas energy. For CHP, net energy represented the total energy produced from heat and power in a reciprocating engine; for RNG, net energy was evaluated based on the methane recovery and parasitic load of the gas upgrading system. Net energy from RNG represented an average of 91-percent of the inlet biogas energy, compared to an average of 78-percent for CHP projects. Significance: Ultimately, the Bioenergy Model is a tool that can be used for the financial evaluation of biogas utilization scenarios and modified to suit a specific plant's needs. All parameters are designed to be adjustable, allowing for variations in the analysis based on digester feed flows, anaerobic digestion kinetics, plant utility costs, and RNG offtake values. Specifically, the Bioenergy Model was designed to evaluate D3 RIN values compared to CHP via a reciprocating engine; the model can be revised to account for specific digester kinetic factors, varying RIN values (e.g., co-digestion of outside sludges for D5 RIN production), additional environmental credit programs (e.g., California low carbon fuel standard, Oregon clean fuels program), greenhouse gas emissions comparison, carbon intensity evaluation, additional sensitivity analysis on inputs, or other cogenerating technologies for detailed biogas utilization analysis.

This paper was presented at the WEF Residuals and Biosolids Conference in Columbus, Ohio, May 24-27, 2022.

SpeakerSchaich, Laurel

Presentation time

9:00:00

9:30:00

Session time

8:30:00

11:45:00

Session number08

Session locationGreater Columbus Convention Center, Columbus, Ohio

TopicBiogas, Cogeneration, Renewable Natural Gas

Author(s)

L. Schaich

Author(s)L. Schaich1; J. Hutchison2

Author affiliation(s)CDM Smith; 1University of Kansas; 2

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May, 2022

DOI10.2175/193864718825158396

Volume / Issue

Content sourceResiduals and Biosolids

Word count11

Base text size -

Schaich, Laurel

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