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Bioenergy Potential from Food Waste in California Hanna Marie Breunig, Ling Jin, Alastair Robinson, and Corinne Donahue Scown Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04591 • Publication Date (Web): 10 Jan 2017 Downloaded from http://pubs.acs.org on January 11, 2017

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Environmental Science & Technology

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Bioenergy Potential from Food Waste in California

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Hanna M. Breunig1*, Ling Jin1, Alastair Robinson1, Corinne D. Scown1,2

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1

Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

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2

Joint BioEnergy Institute, Emeryville, CA, 94608, USA

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*

Corresponding Author

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Address: Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA; MS: 90R2002B

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Phone: (510) 486-4046

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E-mail: [email protected]

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ABSTRACT

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Food waste makes up approximately 15% of municipal solid waste generated in the United States, and

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95% of food waste is ultimately landfilled. Its bioavailable carbon and nutrient content makes it a major

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contributor to landfill methane emissions, but also presents an important opportunity for energy recovery.

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This paper presents the first detailed analysis of monthly food waste generation in California at a county

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level, and its potential contribution to the state’s energy production. Scenarios that rely on excess capacity

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at existing anaerobic digester (AD) and solid biomass combustion facilities, and alternatives that allow for

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new facility construction, are developed and modeled. Potential monthly electricity generation from the

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conversion of gross food waste using a combination of AD and combustion varies from 420 to 700 MW,

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averaging 530 MW. At least 66% of gross high moisture solids and 23% of gross low moisture solids can

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be treated using existing county infrastructure, and this fraction increases to 99% of high moisture solids

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and 55% of low moisture solids if waste can be shipped anywhere within the state. Biogas flaring

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practices at AD facilities can reduce potential energy production by 10 to 40%.

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ABSTRACT ART

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INTRODUCTION

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Inefficiencies occur at all stages of the food supply chain, linked to complex factors ranging from market

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conditions and weather to consumer preferences, and these inefficiencies translate to an abundance of

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food waste. The US generated approximately 38 million tonnes of municipal food waste in 2014,

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approximately 95% of which was landfilled.1 An enormous amount of energy, water, land, and other

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resources go into producing nutrition for humans.2 A recent analysis of food waste estimated that $218

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billion is spent in the US on growing, processing, transporting, and disposing of food and by-products that

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go uneaten.3 Furthermore, because food waste biodegrades four times faster than typical paper products

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and ten times faster than wood waste, it releases methane from landfills more quickly than most other

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organic waste, with 34-51% of generated methane escaping typical landfill gas capture systems.4-6

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Landfilling uneaten solid organic material not only contributes to climate change and occupies land

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resources, but also eliminates the possibility of cycling the valuable nutrients and energy in food back into

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the economy.

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First and foremost, policy measures are necessary to ensure source-reduction through changes in

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consumer behavior and improved harvesting, processing, and transportation methods.7 However, source-

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reduction alone will not be a sufficient strategy. Americans consume raw produce and livestock that have

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both edible and inedible parts, from local and non-local sources, and in quantities that require some level

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of centralized production and distribution. These biological materials can only be used for their original

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purpose - to provide nutrition and sustenance to humans - for a short window of time, and maintaining the

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value of food is not always possible. Food waste-to-energy strategies can help meet renewable energy

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targets, greenhouse gas (GHG) reduction targets, air quality standards, and divert waste from landfills.8

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In this paper, the potential for converting California’s food waste to electrical and thermal energy is

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analyzed, including organic waste from the food supply chain: agricultural production, post-harvest 3 ACS Paragon Plus Environment


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handling and storage, processing and packaging, distribution, consumption, and end-of-life. The

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objectives of this study are to determine the quantity, locations, and temporal variation in food waste

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generation, use these results to model regional and sub-annual electricity and heat generation potential,

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and gain insight into the roles of policy and technology in overcoming challenges associated with food

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waste utilization. California serves as a useful starting point for building an analysis framework that can

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be applied to the US or globally because of its diversity and significance in national food production

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(40% of US vegetables, 20% of dairy, and 70% of fruits, tree nuts, and berry production by revenue).9-10

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Although previous assessments have estimated the total annual energy potential from food waste from

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retail and consumer waste streams and from food processors in California,11-14 this study is the first to

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assess food waste production at the sub-annual scale and to develop a spatially and temporally explicit

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model that integrates feedstock production and energy infrastructure capacity to estimate potential energy

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production. By accounting for infrastructure, logistics, and storage limitations, our study provides a more

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robust assessment of potential electricity and thermal energy generation and highlights key challenges

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that must be overcome to maximize this potential.

69 70

Background and Motivation

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Despite recent drought conditions, California produced over 400 types of agricultural commodities in

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2014.15 With a state population of nearly 40 million people, a large amount of produce is processed and

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consumed in-state. In 2014, 5.2 million wet tonnes of food waste were sent to disposal facilities, up 13%

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from the 4.6 million wet tonnes disposed in 2008.16 Recent federal and state regulatory action has created

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incentives to reduce the generation of organic waste, including food waste, and to divert remaining waste

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to composting and transformation. In 2015, the US Department of Agriculture (USDA) and

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Environmental Protection Agency (EPA) announced the first national food waste reduction goal: 50%

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reduction in post-harvest losses at the retail- and consumer-levels by 2030. New requirements in

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California for source-separation and recycling of commercial organic waste (Assembly Bill (AB) 1826) 4 ACS Paragon Plus Environment

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are intended to reduce GHG emissions and create opportunities for recycling manufacturing facilities;

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however, transformation to energy is not counted towards the statewide 75% solid waste diversion goal

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for 2020, mandated in AB 341. In 2006, Governor Schwarzenegger signed Executive Order S-06-06

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mandating that 20% of renewable electricity comes from biomass; subsequent Bioenergy Action Plans

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have been released to promote technology innovation and guide market development for bio-based

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products and energy.17

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Diverting food waste for energy releases only biogenic carbon and is therefore considered renewable.

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Although some carbon in food waste would otherwise remain sequestered if the waste is landfilled, the

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resulting methane emissions outweigh this sequestration on a 100-year global warming potential (GWP)

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basis.6 Multiple technologies exist for converting organic materials, including food waste, into electricity,

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heat, transportation fuels like hydrogen, and chemical products.18 Extensive reviews have been conducted

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on the anaerobic digestion (AD) of the organic fraction of municipal solid waste,18 and food waste from

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retailers and consumers.19-22 Anaerobic digestion generates a methane-rich biogas and a nutrient-rich solid

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(digestate), the latter of which can be used as a low-carbon fertilizer.23 The methane can be cleaned and

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used onsite to generate electricity and heat in combined heat and power systems (CHP), injected into

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pipelines as renewable natural gas, or compressed into a biological natural gas (bioCNG) transportation

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fuel. Life-cycle assessments of incineration, composting, AD, and landfill treatment technologies for food

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waste find that AD leds to the greatest reduction in carbon dioxide as long as biogas is captured and used

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for energy.23-24 Fats, oils, and greases (FOG) can be used in AD facilities or converted to liquid fuels. For

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example, yellow grease, the used cooking oil from the food industry, is a suitable feedstock for

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biodiesel.25 Not all food waste types are well suited for anaerobic digestion. Waste with moisture content

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(MC) below ~50% and waste with high lignin content are better suited to thermochemical processes like

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combustion and gasification. Combustion of food waste like nut hulls and shells generates heat,

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electricity, and a nutrient-rich ash that can be applied to land. A number of solid biomass power plants

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currently accept low moisture food waste like rice hulls and olive pits.26-27 5 ACS Paragon Plus Environment


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MATERIALS AND METHODS

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Food waste is defined here as organic materials wasted within the food supply chain, including food

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waste generated during harvest, food processing, retail, and in eating establishments and consumers’

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homes. For example, this distinction includes olive pits, but excludes olive tree branches and other

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woody/herbaceous crop residues. In terms of transformation technologies, our study focuses on electricity

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and thermal energy generation. Direct combustion and AD of food waste is assessed because they are the

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most mature conversion technologies for food waste, are capable of handling highly heterogeneous food

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waste streams, and generate products which have established markets in California. Hydrogen or liquid

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fuels could become attractive in the future with technology advancements.28-30

117 118

Food Waste Meta-Analysis and Inventory

119 120

To determine key methodological differences and the data quality/completeness associated with existing

121

studies, a meta-analysis of food waste inventories and energy potential literature is performed (Supporting

122

Information (SI), Sections 1 and 2). Assumptions used to estimate food waste yields (e.g.: fraction of

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MSW that is food waste) and potential electricity and heat generation (e.g.: efficiencies) vary across

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previous assessments of California, and are used to recalculate and compare results.11-14, 31-32 Results from

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previous assessments are compared with data from state33 and national15 agricultural surveys, municipal

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solid waste databases34-35, and personal communication with food bioenergy program managers and food

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processors36-38 to develop the assumptions and methodologies used in this study (SI, Sections 4.4, 4.5, 6).

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39-40

129 130

Waste production data is collection and disaggregation to develop a food waste inventory by month and

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county of origin for California for 2014. For ease of comparison, totals for high moisture solid (HMS) and

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low moisture solid (LMS) wastes are reported in bone dry tonnes (BDT). A bottom-up approach is used 6 ACS Paragon Plus Environment

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to estimate agricultural culls, where county level production data from the 2014 NASS33 for each type of

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produce is multiplied by a “cull multiplier”, which assumes that total available harvest is equal to the sum

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of reported production and cull production (SI, Section 3.1). Planting, harvesting, and peak harvesting

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dates in agricultural regions of California are characterized through a critical review of NASS agricultural

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survey and census data, and plant science literature for each crop (SI, Section 3.6). Cull production is

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distributed evenly over the harvesting time period, unless a peak harvesting or cull collection period is

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identified, in which case 80% of waste mass is distributed over the peak time period. County level food

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processing waste production was reported in a 2007 survey13; annual waste production and locations from

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the survey are adjusted assuming constant waste yields, and that 2013 county level employment data can

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be used to scale 2007 production over time and space.41 New approaches are developed in this study to

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estimate county and monthly waste inventories for meat processors, distilleries, breweries, commercial

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bakeries, tortilla manufacturers, and fruit and olive pitters (SI, Section 3.2), while the approach developed

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in Williams et al. 2015 is used to model nut and rice hullers’ waste.11

146 147

The mass of MSW generated in each county in 2014 is collected by quarter from the CalRecycle disposal

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database (SI, Section 3.3).16 The food waste fraction of MSW generated by retailers and consumers at the

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regional level is taken from a 2014 characterization study (SI, Table S6).39 Fats, oils, and grease

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production at the retail and consumer levels are determined using per-capita annual consumption data and

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waste yields from the USDA Economic Research Service (SI, Section 3.4).42-43 US Census population

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data from 2014 is used to calculate total FOG generation at the county-level.

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Food waste technical availability is determined using two indicators: (1) extent of source-separation

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practices and hauling networks, (2) strength of established markets for wastes (animal feed, rendering,

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etc) (SI, Section 3.5).11-13 Technical availability is high for wastes like winery pomace, which are

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collected and stored during a production process and treated as wastes. Technical availability is very low



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for wastes like almond hulls which are used as animal feed, and low for wastes like vegetable culls which

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are difficult to collect and transport.

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Existing Anaerobic Digester Capacity

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The EPA estimates an excess AD capacity of 15-30% at roughly 140 wastewater treatment facilities in

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California.29, 44 Excess capacity allows facilities to handle fluctuations in wastewater due to weather,

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population growth, and changes that occur when sources of wastewater and waste biomass relocate. Thus,

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near-term diversion of food waste can be achieved with existing AD infrastructure if there is sufficient

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capacity to co-digest food waste alongside other organic feedstocks like wastewater solids.45-46

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Specifically, high moisture solids (MC ≥ 55%), bakery wastes, and wastewater with high-biochemical

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oxygen demand (BOD) content are all candidates for co-digestion. A meta-analysis of California WWTF

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databases and excess capacity estimates is included in the SI, Section 2.

171 172

High-moisture solids are expensive to transport long distances and are challenging and costly to store,

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although technologies are emerging for extending storage periods of food materials including wastes.47

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Therefore it is critical that AD capacity is matched to food waste production at appropriate spatial and

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temporal scales. Potentially available capacity is estimated in this study for utilizing food waste in

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existing organic waste-to-energy facilities with wet, dry, or high-solids AD systems (SI, Section 4).

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Excess capacity in wet AD systems at WWTF (both municipal and private) is determined by calculating

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the potential to increase flowrate if facilities reduced their mean cell residence time (MCRT) to the EPA

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40 CRF Part 503 regulation minimum of 15 days (Equation 1).48 Adequate digestion of solids is

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necessary for producing biosolids that are stable and have low pathogen content, minimizing total

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biosolids produced, and generating biogas with higher methane content. Food waste is more

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biodegradable than wastewater solids, and thus more easily broken down in 15 days, however operators


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should take care when reducing their MCRT as the impact of changes on performance will vary between

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facilities.

185 186

= − ( ) −

Equation 1

187 188

where Q (million liter per day MLD) is the excess volumetric flowrate of food waste that could enter the

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digester, τ is the MCRT used at the facility (days), V is the volume of the digester (million L), and Qdilution

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is the volume of processing water needed to dilute food waste to 8% total solids (TS). Qdilution is adjusted

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in scenario alterative “d” (Table 1).

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Not all facilities provide data on digester volume (V) and operational MCRT (τ), so an approach is

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developed to estimate flow rate increases based on average daily wastewater flowrate (Qinfluent). Data from

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16 facilities in California that provided V, τ, and Qinfluent revealed that excess volumetric flowrate Q could

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increase Qinfluent by 0.1 to 2%.32 These percentages are multiplied by Qinfluent for each WWTF, to give a

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range in excess capacity that is within an order of magnitude of the values calculated using engineering

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principles (Equation 1), and that allows us to model individual facilities, despite limited data. Capacity at

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operating organic waste-to-energy facilities are determined or approximated using available facility data

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on loading rates and waste composition (SI, Section 6).39 Finally, the excess volumetric flowrate Q

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[MLD] at each facility is converted to an excess mass loading rate [BDT/d] (SI, Equations S4-S7) and

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aggregated to the county and state level (SI, Table S17); these rates are treated as constants. County or

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state aggregated food waste production is converted to [BDT/d] for each month or for the whole year

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depending on the storage duration assumed in scenarios ( alternative “f” Table 1).

205 206

Existing Combustion Capacity

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It is assumed that solid fuel biomass power plants operating at less than 90% capacity factor (CF) are

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feedstock constrained and are capable of co-firing any dry biomass to reach a CF of 90%. Capacity

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factors are gathered from 2012 eGRID for all operating solid biomass power plants in California to

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determine excess capacity (Equation 2).27, 49 Power plants are cross-referenced with facility websites when

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possible to confirm operational status and feedstock mass loading rates. Combustion capacity and

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available waste biomass are adjusted for the mass of rice hulls, fruit and olive pits, and nut shells

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currently being diverted to specific solid biomass power plants.

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= (0.9 − ) ∗

Equation 2

217 218

where P is the excess capacity (MW) at facility i, CF is the capacity factor (%), and PNP is the nameplate

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capacity (MW).

220 221

Biogas combustion capacity is assumed to be unconstrained. The analysis is rerun with facility specific

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capacity constraints reflected in biogas flaring practices (SI, Section 4.3).

223 224

Electricity and Thermal Energy Generation Potential

225 226

The six scenarios used to assess food waste bioenergy potential are summarized in Table 1. Potential

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energy generation from the conversion of gross food waste generated in California is estimated in

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Scenario 1. Potential energy generation from the conversion of only technically available food waste is

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estimated in Scenario 2. Scenario 1 and 2 parallel the type of scenarios used in previous assessments.1`

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Scenarios 3 assumes that food waste and FOG separated out of MSW are directed to organic waste-to-

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energy facilities until the statewide excess mass loading rate is met. Food waste and FOG not sent to

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organic waste-to-energy facilities are then sent to wet AD systems at WWTFs within the state. If excess

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capacity is still available, treatment of municipal wastes is followed by loading of state specific blends of 10 ACS Paragon Plus Environment

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processor food waste and then culls. This hierarchy is based on the following rationale: MSW is

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generated in urban areas near WWTFs and have centralized collection and hauling networks; organic

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waste-to-energy facilities have large upfront capital costs and have likely established contracts with MSW

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haulers; there are collection and disposal systems in place for food processors and less so for in-field

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culls. Scenario 4 is the same as Scenario 3 except food waste transportation and treatment is constrained

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to the county of origin. Scenarios 5 and 6 are the same as Scenarios 3 and 4, respectively, but evaluate

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energy generation from technically available food waste. Scenario 6 is the most conservative scenario, as

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it assumes energy generation competes poorly with other markets for food waste, and that waste haulers

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will not export food waste to facilities in nearby counties. Variations b-f on Scenarios 3-6 reflect different

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operation practices at WWTF, and different storage limitations on HMS, than the base case (variation a).

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Methane production in Scenarios 1 and 2 is modeled using wet-AD methane yield factors for specific

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types of HMS (including commercial bakery and tortilla wastes) (SI, Table S8). In the other scenarios,

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methane yields specific to the type of AD technologies at waste-to-energy facilities are used to model

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digestion of MWS food waste and FOG (SI, Table S10). Without in situ data, proxies are needed to

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estimate methane yields from blends of food waste collected throughout the state (Scenarios 3 and 5) or

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county (Scenarios 4 and 6) and sent to wet AD systems at WWTF. To develop these proxies, waste-

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specific methane yields are scaled by the volatile solids fraction in blends of culls or blends of food

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processor wastes and summed to estimate the methane yields for the blend.

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Survey data is used to identify the type of CHP technology that is or potentially would be used at different

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scale facilities. Small facilities mainly use rich-burn and lean-burn engines, whereas large scale facilities

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use engines as well as microturbines, and fuel cells.50 Electric efficiency and average power-to-heat ratios

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are acquired from program performance standards (SI, Section 2.2).51 A CHP capacity factor of 85% and

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an energy content of 38.3 MJ/m3 (1027 BTU/ft3) methane are assumed. Combustion turbines, steam

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turbines, and combined cycles prime movers for CHP are not included as they are uncommon at 11 ACS Paragon Plus Environment


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WWTF,51 and because heat demand at WWTF reduces waste heat availability. Annual loss of methane

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due to flaring and fugitive emissions is estimated for each WWTF based on biogas utilization and flaring

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survey data (SI, Section 4.3).52 Net electricity generation from LMS is calculated based on an efficiency

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of 0.2, and using waste-specific dry-basis higher heating values (HHV) (SI, Table S7).11-13 It is assumed

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that LMS can be stored for up to a year to achieve steady loading rates to solid biofuel power plants in

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Scenarios 3-6. Additional details of the sensitivity analysis are provided in Table S18.

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Table 1. Electricity and heat generation scenarios and constraints. Specific variations in sensitivity analysis are given

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a Variation Number if they are shown in Figure 1. HMS = high moisture solids; TS = total solids.

Scenario Number & Description

Feedstock Availability

Transport. Extent

Sensitivity Analysis Parameter

Base Case

Variation

Variation Label

1

Energy from all food waste

Gross

-

WWTF influent scaling factor used to estimate food waste loading rates into AD

2% Qinfluent

0.1% Qinfluent

b

2

Energy from technically available food waste

Tech.

-

Total solids specification for food waste slurry loaded into AD

8% TS

no spec.

c

3a

Energy from all food waste that can be treated using existing California infrastructure

Gross

In-State

Total solids specification for food waste slurry loaded into AD

8% TS

4% TS

d

4a

Energy from all food waste that can be treated using existing county infrastructure

Gross

In-County

HMS storage duration limit

1 month

1 year

e

5a

Energy from all technically available food waste that can be treated using existing California infrastructure

Tech.

In-State

methane utilization**

85%

70-95%

f

6a

Energy from all technically available food waste that can be treated using existing county infrastructure

Tech.

In-County

*Limited to the fraction of waste that isn’t directed to other uses and could be collected (technically available). **For facilities that report biogas utilization. Facilities without biogas utilization are set at 0% utilization.

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RESULTS

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Meta-Analysis

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Recent assessments have estimated gross production of food waste from MSW and from food processors

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in California at the state and annual level, as well as the fraction that is technically available for energy

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production (SI, Section 1).11-13 With the inclusion of culled produce and new categories of food

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processing wastes, this study estimates 20% higher gross production of food waste. Previous studies have

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used combustion and AD to model energy production; however, this study is the first to model facility-

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specific excess combustion and AD capacity, technology- and feedstock-specific methane yields, and

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storage and transportation impacts. Despite generation potential varying between studies due to

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differences in food waste production, AD methane yields, and methane energy content, this study

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estimates a very similar generation potential from gross food waste (Scenario 1) as William et al. (Table

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2).11 The range in excess AD capacity at WWTFs estimated in this study bounds values in previous

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assessments. Kester estimates that that 75% of all food waste from MSW could be treated in-state at

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WWTF (~4,100 BDT/d). Using this estimate, it would appear that WWTF could treat 100% of technically

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available food waste from MSW (~2,570 BDT/d 11 to ~3,030 BDT/d in this study). This study finds that

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99% of technically available food waste from MSW could be treated in-county using the most optimistic

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excess capacity assumptions. However, capacity assumptions that reflect existing food waste treatment

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projects ongoing in the state (Scenario 4b) resulted in treatment of only 23% of technically available food

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waste from MSW.

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Table 2. Previous assessments of food waste resource and energy potential in California. BDT = bone dry tonnes;

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FOG = fats, oils, grease. [Matteson & Jenkins 2007]12 Reference Gross Production [10⁶ BDT/y] Culls Processors 1.2 MSW - food waste 2.0 MSW - FOG Electricity Generation [MWe] Culls Processors 134 MSW - food waste 105 MSW - FOG Sum [MWe] 239

[Williams et al. 2015]11

[Amon et al. 2012]13

[Kester 2015]32

4.0 1.3 -

3.2 -

-

549 184 733

534 534

-

0.9 0.01

This Study [Scenario #1]

0.8 4.4 1.1 0.004 97 527 98 2 724

294 295

Food Waste Inventory

296 297

The monthly variation in HMS food waste production is less for food waste and FOG in MSW, spent

298

grains, and meat residues than it is for culls and fruit and vegetable processors (Figure S6, Figure S7).

299

Production of LMS varies significantly between harvest and non-harvest dates (Figure S8) and between

300

urban and agricultural counties (Figure S9). Gross waste from food processors totals 4.4 million BDT/y;

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for comparison, Williams et al. calculated a gross food processing residue generation of 3.9 million BDT

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in 2013.11 Statewide, 1.1 million BDT/y of food waste and 3,560 BDT/y FOG is generated in MSW. Food

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waste from MSW is generated in all 58 counties in 2014, with the lowest production rate occurred in

304

Alpine County in the fall (Oct-Dec) at 0.1 BDT/d and the highest production rate occurred during the

305

same period in Los Angeles County (813 BDT/d). Uncertainty in the location of production is lowest for

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MSW and culls and highest for food processor wastes, while uncertainty in tonnage is moderate for MSW

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and high for food processor wastes and culls. Food waste tonnage by type, county and month is included

308

in SI Section 5, Tables S13-S16 while statewide annual tonnage is provided in Table S19.

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Electricity and Thermal Energy Generation Potential

311 312

The energy in all food waste generated in California could supply monthly electricity generation varying

313

from 210 MWe in March to 1,490 MWe in September, with an annual average of 1,120 MWe that is

314

equivalent to 55% of installed biomass electric generation capacity in California (Scenario 1, Figure 1).53

315

Over 22 GJ of waste heat can be generated annually in addition to electricity. Nearly all gross HMS

316

(99%) and 55% of gross LMS (including 97% of all rice hulls) could be converted to electricity and heat

317

using only existing excess capacity, or capacity already dedicated to treating food waste like rice hulls

318

(Scenario 3a). Multi-month storage of LMS evens out a majority of the seasonality seen in treatment of

319

gross food waste (Scenario 1), resulting in a higher baseline at the state level. Electricity generation from

320

technically available food waste (Scenario 2) is substantially lower than it is for gross food waste

321

(Scenario 1), largely due to complete diversion of almond hulls to animal feed (Figure 1). Seasonality was

322

even less pronounced in Scenario 5a, where technically available food waste is treated using existing

323

excess capacity, as only 10% of culls are assumed to be diverted to bioenergy.

324 325

Surprisingly, the biogas yield from state blends of culls is relatively stable over the year, varying from

326

0.29 to 0.36 m3/kg TS (SI, Table S9). There is a slight dip in the summer due to production of an

327

enormous amount of tomato culls, which have a low biogas yield, however root and tuber culls, which

328

have a 36% higher biogas yield, are produced at the same time and help to offset the impact. Biogas yield

329

from the state blend of food processing wastes stays around 0.44 m3/kg TS (high due to meat processing

330

residues and spent grains), except for a noticeable dip from July through October when it drops to 0.34

331

m3/kg TS. This dip represents the most active season for wineries and fruit and vegetable processors,

332

which generate residues with low biogas yields.

333 334

For county level treatment of gross and technically available food waste (Scenario 4a and Scenario 6a),

335

monthly biogas yields are unique to each county and vary from 0.26 to 0.43 m3/kg TS for cull blends and 15 ACS Paragon Plus Environment


Page 16 of 30

336

from 0.24 to 0.51 m3/kg TS for food processing residue blends. Seasonal variation in biogas yields

337

resulting from changing composition of culls and food processing residues is limited in some counties and

338

pronounced in others.

339 340

Mismatch between the location of food waste production and the location of energy facilities resulted in

341

~60% lower average electrical generation in Scenario 4a than in Scenario 1 (Figure 2). Generation

342

potential from HMS is further reduced by 25% when methane losses due to facility specific flaring

343

practices are included (ranging from 11 to 38%). Local combustion capacity is limited in the Central

344

Valley, where over 95% of gross LMS is produced; only 23% of gross LMS can be converted to energy

345

in Scenario 4a even with multi-month storage.

346 347

Calculations of existing excess AD capacity is more sensitive to changes in the food waste to wastewater

348

volumetric loading ratio (Scenario 3b) than the total solids specification for the food waste slurry dilution

349

(Scenario 3b,c). Scenario 3b reflects the operational practices used at WWTF currently accepting HMS;

350

under these conditions, only food waste and FOG from MSW are treated, generating 25 MW. Wastewater

351

from food processors is not included in these totals as it is unclear what fraction is already being treated at

352

WWTFs (SI, Section 3.2.6). Annual wastewater from food processors contains 158,410 BDT of BOD5

353

and has the potential generate 56 million m3 of methane if 100% is co-digested (20 MWe and 640 MJ

354

waste heat).


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355 356

Figure 1. Total food waste converted to energy and total electricity generation potential per month are shown for

357

Scenarios 1-6. Scenarios and variations are described in the table below the figure. Months are abbreviated in the x

358

axis labels (J=January, A=April, J=July, O=October). Values are disaggregated by region and food waste type in SI,

359

Section 7.



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360 361

Figure 2. Facility-level electricity generation capacities for treating food waste are mapped over county-level annual

362

food waste-to-energy potential (Scenario 1e). New solid biomass combustion capacity and new AD infrastructure

363

(converted to biogas combustion capacity [MWe] for ease of comparison) needed to reach gross potentials are

364

shown in the maps on the right. Values reflect the assumption that facilities with existing excess AD capacity have

365

unconstrained combustion capacity. Facility addresses and counties mapped using 2016 TIGER shapefiles.54

366 367

DISCUSSION

368 369

This study assesses the use of AD and direct combustion to convert food waste into electricity and

370

thermal energy in California. Between 10% and 99% of gross HMS and can be digested using state AD

371

infrastructure and in the same month of production, and between 10% and 66% can be digested in-county

372

using AD infrastructure and in the same month of production. These large ranges reflect the uncertainty

373

regarding excess capacity for food waste co-digestion at WWTFs and organic waste-to-energy facilities.

374

Accounting for technical availability (removing losses and currently utilized fractions) for waste best

375

suited for AD results in potential utilization ranging from 37-100% for in-state, and to 37-99% for in-

376

county. Only 45% of gross LMS (including 80% of total rice hulls) can be converted to energy using


Page 19 of 30


377

excess capacity at in-state solid biomass power plants, while only 27% can be converted to energy if LMS

378

must be utilized within the county of origin. This is concerning as over 90 MW of solid biomass installed

379

capacity is going offline in 2016 alone.55 Additional LMS waste from forests, resulting from recent

380

droughts and bark beetle infestations, will result in even more competition at composting and organic

381

transformation facilities and solid biomass power plants.56 Fuel-switching at natural gas power plants is a

382

possible solution for decreasing the new capacity needed to handle LMS, but will likely require policy

383

incentives, given the cost of retrofitting equipment for fuel-switching.57

384 385

As shown in Figure 2, the construction of 122 MWe of new AD capacity is still necessary for in-county

386

utilization, even with the possibility of multi-month storage of HMS, as a number of counties with high

387

cull and processor waste production have low populations and thus low AD capacity at local WWTFs.

388

Widespread storage of HMS at AD treatment facilities in urban areas is unlikely in the near future due to

389

cost, odor, and health concerns.

390 391

Uncertainty, Data Gaps, and Future Work

392 393

Increased availability of data on waste generation, and AD system capacity and operation would help

394

reduce the uncertainty associated with estimates of energy generation potential. The temporal and spatial

395

specificity of input data used to develop the food waste inventory is not uniform. Input data is collected

396

from annual agricultural surveys, quarterly MSW disposal reports, and annual employment and per capita

397

FOG consumption data. Input data is reported at the county level, while harvesting periods and MSW

398

composition are reported at the regional level. Data needed to estimate excess capacity in AD systems is

399

limited, and the uncertainty is bracketed through the use of low and high estimates in scenarios. Even

400

with optimistic assumptions regarding available existing excess capacity, twenty-three counties are likely

401

to be capacity-limited at WWTFs year round, and 13 counties are capacity-limited at WWTFs during part

402

of the year, resulting in 0.8 million BDT/y of food waste going untreated. Capacity at AD systems at food 19 ACS Paragon Plus Environment


Page 20 of 30

403

processing facilities, proposed food transformation facilities, and at dairy operations may be available to

404

treat this waste, but are not included in this study due to limited data on current waste practices and AD

405

design. Personal communication with operations managers at each facility will be essential to filling this

406

data gap. Similar to the progression of the Billion-Ton studies, future work will estimate food waste fuel

407

price points and system costs to constrain generation potential.25

408 409

The fraction of biogas currently generated at WWTF that is flared or vented remains another key source

410

of uncertainty. It is a federal regulation that AD systems have flaring equipment installed and meet a

411

conversion efficiency of methane to carbon dioxide between 95-99% during flaring events. Some

412

facilities (~30%) use the biogas for onsite CHP, but most facilities do not have CHP technology and

413

either combust biogas in boilers (~40%) or flare the biogas (~30%).50 In 2011, total CHP capacity at

414

WWTF in California was 63 MW.51 The use of flaring is driven by a range of factors including biogas

415

quality56 and gas storage capacity.36 Data on deliberate venting is of course limited, as is data on fugitive

416

methane emissions (leakage). An approximation is developed for modeling flaring of biogas (SI, Section

417

4.3), which revealed that nearly 100% of biogas from WWTF is flared, vented, or lost in eleven counties.

418

Such practices need to change to capture the energy potential of food waste digestion.

419 420

Policy Recommendations

421 422

Policy incentives to encourage (1) the separation of food waste from MSW streams, (2) the

423

transformation of food waste to energy (by including energy production as a diversion option in recycling

424

policies), and (3) higher market values for energy by-products, will be key in reducing food waste

425

disposal and increasing food waste energy production. Regarding the third point, organic waste-to-energy

426

facilities facing seasonal or weak markets for compost and by-products are at risk if local policies prohibit

427

food waste by-products from entering landfills (e.g.: Alameda County ACWMA Ordinance 2012-01).

428

Furthermore, standards prohibiting the mixing of food waste with biosolids from human waste force AD 20 ACS Paragon Plus Environment

Page 21 of 30


429

facilities to reserve whole digesters for food waste treatment. Some barriers, like underreporting biogas

430

flaring at AD facilities and incentivizing waste haulers to deliver organics to recycling and energy

431

facilities may be quicker to resolve through policy than barriers like limited facility space for pre-

432

processing and combustion equipment. Educational outreach to food processors, farmers, and WWTF

433

managers is needed, as these actors frequently do not have the resources or experience to determine the

434

best course of action for their unique waste streams or for becoming energy generators.

435 436

Global Perspective

437 438

As this study demonstrates, the importance of localized food waste management and data collection

439

cannot be understated, as potential energy generation is dependent on the availability of blends of food

440

waste which are suitable for processing in nearby systems with existing excess handling and conversion

441

capacity. Developing countries generally lack centralized waste, recycling, wastewater, and energy

442

infrastructure and regulation that could manage food waste at economies of scale.58 Industrialization leads

443

to urbanization, as well as increasing per capita food consumption,59-60 and resulting growth and

444

diversification of food supply chains will generate increasing quantities of food waste. Solutions broader

445

than farm and home scaled biogas units will be needed to manage the waste.57 Research on the food,

446

energy, and water nexus at the local and regional level can help stakeholders identify breakthroughs in

447

technology and policy that provide food security, while enabling economic and sustainable flows of

448

nutrients and energy.

449 450

Supporting Information

451 452

The Supporting Information (SI) is available free of charge on the ACS Publications website. The SI

453

includes: details of the meta-analysis, methods, and results sections, and a discussion on wastewater from

454

food processors. 21 ACS Paragon Plus Environment


Page 22 of 30

455 456

Acknowledgements

457 458

The research for this paper was financially supported by the California Energy Commission under

459

agreement number EPC-14-030. We would like to thank S. Sherman, G. Kester, E. Bariani, K. Piscopo,

460

N. Carr, H. Youngs, T. Pray, and P. Sethi for their insight and assistance gathering data. This work was

461

also part of the DOE Joint BioEnergy Institute (http:// www.jbei.org) supported by the U. S. Department

462

of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-

463

AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U. S. Department of Energy.

464

The United States Government retains and the publisher, by accepting the article for publication,

465

acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-

466

wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for

467

United States Government purposes.


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