Minimization of Resource Consumption and Carbon Footprint of a

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Minimization of the resource consumption and carbon footprint of a circular organic waste valorization system Selene Cobo, Antonio Dominguez-Ramos, and Angel Irabien ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.7b03767 • Publication Date (Web): 22 Jan 2018 Downloaded from http://pubs.acs.org on January 24, 2018

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ACS Sustainable Chemistry & Engineering

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Minimization of the resource consumption

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and carbon footprint of a circular organic

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waste valorization system

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Selene Cobo, Antonio Dominguez-Ramos and Angel Irabien*

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Department of Chemical and Biomolecular Engineering, University of Cantabria

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Avda. los Castros s.n., Santander, 39005, Spain

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*Corresponding author: Angel Irabien

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Tel.: +34 942 20 15 97. E-mail: [email protected]

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ABSTRACT

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The efficient management of municipal organic waste (OW) will contribute to the transition to

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a circular economy of nutrients. The goal of this work is to determine the optimal

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configuration of a waste management system that valorizes the OW generated in Cantabria.

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The model was developed with the EASETECH and the DNDC softwares, and it assumes that

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the products generated from the OW (compost, digestate, struvite and ammonium sulphate)

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are applied to land to grow corn. The closed-loop perspective of the system is given by the

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application of these products, which results in a reduction in the consumption of the industrial

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fertilizers required for the production of food, a fraction of which becomes OW in a later life

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cycle stage. A superstructure comprising technologies to manage OW was developed. A MILP

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problem was formulated for the multi-objective optimization of the flows of OW that are sent

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to each technology according to these objective functions to be minimized: the carbon

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footprint of the system (CF), the landfill area occupied by OW (LFA) and the consumption of

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non-renewable raw materials (NR-RM). It was found that a combination of different

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technologies is required to attain a trade-off between the objective functions. The

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minimization of the CF leads to a system configuration with a high N circularity and the

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maximal values of LFA and NR-RM, whereas the minimal consumption of NR-RM is achieved at

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the scenarios with low N recovery rates. This indicates that an enhanced circularity of

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resources does not necessarily entail that the overall consumption of natural resources and

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the emission of environmental burdens of the system decrease.

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KEYWORDS

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Organic waste, circular economy, nutrient recovery, systems optimization, carbon footprint

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INTRODUCTION

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The transition to a circular economy could relieve the pressure on the ecosystems to meet the

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demand for natural resources. Thus, the implementation of systems that strengthen the

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connection between waste management and the transformation of raw materials, hereafter

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referred to as Circular Integrated Waste Management Systems (CIWMSs), should be

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promoted.1 CIWMSs provide a solid framework to assess the consequences of the recirculation

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of the waste components.

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The application of the concept of CIWMSs to the management of organic waste (OW), also

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known as bio-waste, is particularly challenging because of the diversity of materials that it may

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contain and its high moisture content. Nevertheless, due to its carbon (C) rich composition and

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the presence of nutrients such as nitrogen (N) and phosphorus (P), energy and nutrients can be

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produced from OW. State-of-the-art research focuses on the production of chemicals and fuels

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from OW.2-4

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Closing the loop of nutrients to a certain extent would help to secure the food supply. N and P

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are essential to the metabolism of plants, and by extension, to agriculture and food production

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systems. Paradoxically, human tampering with the N and P biogeochemical cycles, mostly due

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to the inefficient production and use of fertilizers, leads to eutrophication problems that affect

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the aquatic food chains,5 whereas the remaining accessible reserves of clean phosphate rock

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could run out as soon as 50 years from now.6 Although N is an abundant element in the

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atmosphere, the synthetic production of N-based fertilizers is an energy intensive process.7

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Hence, the substitution of the N recovered from waste for N-fertilizers could potentially

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contribute to climate change mitigation.

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Despite the benefits that a circular economy of nutrients offers, without policies to support the

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circularity of resources, this is not likely to become the priority of the stakeholders involved in

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waste management. One of the local resources that has more influence on the configuration of

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integrated waste management systems is land. Moreover, the ecosystem around the area that

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has been used as a landfill is severely degraded and the site has very limited applications.

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Although landfills can never be completely avoided,8 a well-designed CIWMS should minimize

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their use.

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This discussion is of interest for the region of Cantabria, located in the northern coast of Spain,

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since the government is studying the possibility of expanding the existing landfill to guarantee

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its lifespan. The OW generated in Cantabria is sorted out from the inorganic fraction of mixed

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household waste and composted at a mechanical-biological treatment facility. The agricultural

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application of the resulting bio-stabilized material entails certain environmental risks

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associated with the transfer of heavy metals and organic pollutants to the soil.9 After the

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application of Directive 2008/98/EC,10 which was transposed into the Spanish Law 22/2011

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about waste and polluted soil,11 a distinction between the bio-stabilized material and the

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compost generated from the source-separated OW is made; the former cannot be applied to

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land. However, the Cantabrian waste managers were granted an authorization to continue

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with this practice.12 Its expiration in early 2018 poses the unanswered question of how to

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manage the OW generated in Cantabria.

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The objective of this work is twofold: i) to propose a methodological framework to address

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some of the sustainability challenges related to the management of OW, and ii) to assist

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decision-makers in selecting the optimal configuration of a CIWMS that aims at valorizing the

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OW generated yearly in Cantabrian households. The optimal configuration of the system is

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defined as the combination of nutrient and energy recovery technologies that minimize these

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three objective functions: climate change impacts, land use and consumption of raw materials.

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The potential of systems engineering to establish a connection between resource and waste is

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recognized in the literature.13-15 However, the studies that seek to optimize waste

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management systems only consider environmental criteria.16-20 The novelty of this research is

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that the problem is also approached from the perspective of the minimization of the

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consumption of natural resources. To the best of the authors’ knowledge, waste management

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has never been analyzed from the viewpoint of a CIWMS that includes within its boundaries

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the upstream processes responsible for the delivery of waste and the transformation of the

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recovered waste components.

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The paper is structured as follows. First, the system under study is described. Then, the

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methodological approach is defined, and the hypothesis regarding the life cycle model and the

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problem formulation are provided. Finally, the results are presented and discussed.

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SYSTEM DESCRIPTION

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The superstructure shown in Figure 1 accounts for the alternative technologies to handle OW

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within the studied CIWMS. The unit processes whose input flow is a decision variable have

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been shaded in green. The solution to the optimization problem will determine the flows of

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OW that must be sent to each unit process to achieve optimal results. The CIWMS described in

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Figure 1 also includes the agricultural application of the products recovered from OW, and the

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remaining Cantabrian food production and consumption subsystem, responsible for the

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generation of OW. The dotted line in Figure 1 represents the boundary that separates the

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CIWMS from the environment. They are connected through the consumption of natural

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resources and the emission of environmental burdens of the system, which have not been

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shown in Figure 1 because of their large number.

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Over half of the OW generated in Cantabria is food waste (see waste composition in Appendix

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A of the Supporting Information). It ends up in the Cantabrian bins mixed with other organic

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materials (yard waste and wood) and inorganic residues. The CIWMS comprises two waste

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collection systems: commingled waste and source separated OW (SS-OW).

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The OW recovered from the mixed waste stream (mix-OW) is separated from the inorganic

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materials via trommel screen. Ferrous and non-ferrours metals are previously sorted from the

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mixed waste stream with magnetic and Eddy current separators respectively. The processing

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of the metals and the rest of the inorganic materials is outside the scope of this study. The SS-

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OW does not require any pretreatment, except for the fraction that is subjected to anaerobic

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digestion.21-25, which requires a trommel screen to remove the inorganic materials and avoid

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the transfer of toxic elements from the digestate to the soil. The composting technologies do

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not require any specific pretreatment, because the rejects are screened after the final curing

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or maturation phase.

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Two types of composting technologies were studied: enclosed windrows and tunnel

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composting. Both technologies count with a biofilter to treat the gases and a turner that

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agitates the feedstock to ensure its aerobic degradation. The blue lines in Figure 1 represent

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the flows of OW that cannot be composted after 2018.

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The modeled anaerobic digestion process was based on a wet one-stage thermophilic

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anaerobic digestor. The generated biogas is combusted to produce electricity, and the

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digestate is dewatered through a screw press. The nutrients in the resulting liquor can be 5 ACS Paragon Plus Environment

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Figure 1. System boundaries and superstructure The unit processes represented with a discontinuous line are already available in Cantabria’s waste management system

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recovered via ammonia stripping and absorption (as ammonium sulphate) or struvite

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precipitation. Alternatively, the liquor may be sent to an existing wastewater treatment plant

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for sewage water, which can also receive the residual liquid from the above-mentioned unit

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processes.

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The OW can also be incinerated or disposed of in a non-hazardous landfill, along with the

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rejects of the composting processes and the OW rejected at the pretreatment stage of the

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anaerobic digestion. Incineration is modeled as a grate furnace with wet flue gas cleaning,

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SNCR and activated carbon to treat the flue gas. The released energy is sold as electricity. The

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fly ash undergoes a solidification/stabilization process with cement and water prior to its

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disposal in a mono-landfill, whereas the bottom ash is disposed of in the non-hazardous landfill

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after the removal of metals with magnetic and Eddy current separators. The landfill has

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systems for leachate collection and treatment and biogas combustion and treatment for

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power generation.

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The products generated from the OW (compost, digestate, struvite and ammonium sulphate)

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are applied to land to grow corn. This cereal was selected because it is the main fodder crop in

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Cantabria.26 The nutrients recovered from the OW are not enough to fertilize the land

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available in Cantabria for corn production. Hence, the use of industrial fertilizers is imperative.

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However, as the circularity of nutrients increases, the need for industrial fertilizers decreases.

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The produced corn enters the food production and consumption subsystem, which accounts

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for all the food commodities. It is mainly used as forage for livestock, but it may also be

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processed by the food industry or directly sold to consumers.

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METHODS

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Once the superstructure of the system and its boundaries were established, a mass balance

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model was developed in GAMS 24.8.1. Figure 2 provides an overview of the sequence of

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methodological steps taken.

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The Life Cycle Assessment (LCA) methodology was followed to account for the consumption of

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natural resources and the emission of environmental burdens of the system. An individual LCA

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was carried out for each unit process, and the results were exported to GAMS as model

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parameters. The EASETECH (Environmental Assessment System for Environmental 7 ACS Paragon Plus Environment


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Technologies) 2.3.6 software27

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concerning the treatment of solid OW and the land application subsystem, which are

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dependent on the waste composition, and ii) performing a material flow analysis (MFA) of the

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system. Appendix B compiles the parameters and assumptions made, including the data taken

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from the literature to model the trommel separation,12,28,29 anaerobic digestion,30,31 struvite

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precipitation,32,33 ammonia stripping and absorption34 and transport35 unit processes. The LCA

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results were compiled in Appendix C.

enabled i) obtaining LCA results for the unit processes

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The DNDC (Denitrification-Decomposition) software models the C and N biogeochemical cycles

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in agricultural ecosystems.36 DNDC 9.5 was used to predict corn yield, C sequestration, nitrate

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leaching losses and emissions of C and N gases associated with corn production and the

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application of the fertilizing products to land. These data were subsequently introduced in

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EASETECH, to be translated into environmental impacts. More information about the modeling

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procedure for these subsystems can be found in Appendix D, which includes the DNDC input

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parameters taken from the literature.37-40

EASETECH

DNDC v 9.5

v 2.3.6

GAMS v 24.8.1

MFA & LCA of waste technologies

Model optimization

C and N flow modeling in agricultural ecosystems

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Figure 2. Simplified methodological steps

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Modeling in detail the Cantabrian food production and consumption subsystem is outside the

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scope of this work. It was described with the data provided by Ivanova et al.41 in their study on

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the environmental footprints of European regions.42

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Life cycle model

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The goal of a CIWMS is not waste treatment, but waste valorization through the recirculation

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of the waste components to the upstream subsystems. Thus, the primary function of the

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studied system is land fertilization (which is achieved by means of the combined application of

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industrial fertilizers and the products obtained from the valorization of OW), whereas the 8 ACS Paragon Plus Environment

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secondary system function is energy generation. The selected functional unit to perform the

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LCA of the system is the area available to grow corn in Cantabria (4810 ha).43

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The direct substitution method is applied by expanding the system boundaries to include the

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generation of electricity from the Spanish grid mix. The Spanish legislation prioritizes electricity

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from renewable resources over electricity derived from fossil fuels. Although the biogas

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produced at landfills and anaerobic digestion facilities is considered a renewable energy

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source, the electricity generated from waste incineration does not have priority access to the

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grid.44 Nonetheless, foreseeing the consequences of connecting to the grid another power

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source is outside the scope of the study, whose modeling framework is based on an

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attributional approach. Hence, a 100% substitution ratio was assumed.

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The model applied to characterize the impact of each emission was the hierarchical 100-year

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perspective of ReCiPe 1.11. The results of the global warming impact category strongly rely on

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the hypothesis that only the biogenic C present in animal and vegetable food waste is

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considered neutral. Neutrality implies that the CO2 that is withdrawn from the atmosphere

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during photosynthesis is accounted for as negative CO2 in the life cycle inventory. Since the

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upstream processes concerning the production of other materials present in the OW, such as

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paper, were not modeled, it is not correct to consider the environmental benefits associated

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with the life cycle stage involved in the capture of CO2 by biomass, while the environmental

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impacts of the other life cycle stages of these materials are not quantified.

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One of the main limitations of the proposed model is that the life cycle impacts related to

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capital goods were not considered. The study performed by Brogaard and Christensen45

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concluded that, although capital goods should always be included in the LCA modeling of

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waste management, their contribution to the results of the global warming impact category

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may be negligible.

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Problem formulation

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A Mixed Integer Linear Programming problem was formulated for the optimization of the

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material flows that enter each unit process in Figure 1, according to the following indicators

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that were considered as objective functions to be minimized:

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-

The carbon footprint of the system (CF).



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-

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The consumption of non-renewable raw materials required for the operation of the

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system (NR-RM). This definition excludes the raw materials used for energy

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production, such as coal.

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-

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The landfill area where household OW and the rejects and ashes generated from the management of OW are disposed of (LFA).

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For the set i of indicators and the set j of unit processes, the objective functions (ܱ‫ܨ‬௜ ) were calculated multiplying the amount of waste that each unit process handles (ܹ௝ ) by the

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indicators (ܵ௜௝ ) related to the treatment of 1 ton of waste by each unit process, as shown in

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equation 1. ௡

ܱ‫ܨ‬௜ = ෍ ܹ௝ · ܵ௜௝

(1)

௝ୀଵ

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The problem is subjected to these restrictions: -

The maximum amount of biodegradable waste sent to landfill. Directive 1999/31/EC46

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establishes that biodegradable municipal waste going to landfills must be reduced to

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35% of the total amount produced in 1995.

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-

waste management plan is to recycle 50% of OW before 2020.12

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-

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The minimum amount of OW recycled. One of the objectives set by the Cantabrian

A waste stream of a given composition cannot be split between tunnel and windrows composting.

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SS-OW and mix-OW cannot be mixed in the composting process.

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The Ɛ-constraint method was applied for the multi-objective optimization of the problem.47

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More details about the problem formulation can be found in Appendix E.

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Performance indicators

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The fraction of the N present in waste that is recovered and assimilated by corn could be an

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appropriate indicator to measure the circularity of N within the system. However, its value

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does not only rely on the efficiency of the technological system, but also on the ability of

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plants to capture nutrients from the soil. Thus, a circularity indicator based solely on

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parameters under the control of the decision-makers was developed: the fraction of N that is

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recovered from waste and applied to land with respect to the N present in the collected waste. 10 ACS Paragon Plus Environment

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It is hereafter referred to as N recovery, and it is expressed as kg of recovered N per kg of N in

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waste. This indicator was not selected as an objective function because the consequences of

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increasing the circularity of N cannot be foreseen a priori; it might not lead to a minimization

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of the environmental impacts and the consumption of natural resources.

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Another indicator was developed to compare the circularity of N to the wasted N within the

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system: the efficiency of the corn N uptake (η). It was defined as the fraction of N that is

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absorbed by corn with respect to the available N for corn production within the system, which

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is the sum of the N that comes into the system via fertilizers intended for corn production, and

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the N present in the collected waste. The amount of available N that is not uptaken by corn (1

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– η times the available N) is lost throughout the system. These losses can be stored in soil or

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released to the environment as gas emissions or leachate. The fractions of N that end up in

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each sink depend on the type of product that is applied to soil, as shown in Appendix D. The N

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losses of the food production and consumption subsystem, which are not quantified, may also

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come into other systems as sewage sludge or industrial waste.

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Definition of scenarios

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The collection of SS-OW requires the active participation of citizens, which is the reason it is

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hard to estimate the extension of its implantation. It is assumed that the composition of the

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SS-OW is 98% organic matter and 2% impurities.48 Different source separation rates (SSRs)

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were assessed: 20%, 50% and 80%. For each studied SSR, a pre-Directive and a post-Directive

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scenario (before and after the expiration of the Cantabrian authorization to apply to land the

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compost produced from the mix-OW) were analyzed.

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The expiration of this authorization implies that only SS-OW can be recycled. Thus, the

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recycling objective of 50% of the OW will not be achieved unless at least a 50% SSR is

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implemented in the post-Directive scenarios. Consequently, only two of the six studied

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scenarios comply with the legislation and all the restrictions of the model: the post-Directive

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scenarios with 50% and 80% SSRs.

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RESULTS AND DISCUSSION

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The CF and the consumption of NR-RM of the food production and consumption subsystem

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(excluding corn production), which are detailed in Appendix C, are constant regardless of the 11 ACS Paragon Plus Environment


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value of the optimized variables, because they are taken from the literature.29,30 Their values

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are three orders of magnitude larger than those of the remaining system. Hence, the results

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presented in this section do not include the values associated with the food production and

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consumption subsystem.

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Single-objective optimization

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Figure 3 shows the normalized values of the objective functions obtained as a result of the

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three single-objective optimizations performed for each scenario. The minimal values of each

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objective function are obtained for the highest SSR, because the flows of waste that the

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system manages are smaller compared to those of lower SSRs, on account of the fewer

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inorganic materials that the waste streams contain.

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The minimal CF is achieved at the expense of maximizing the consumption of NR-RM and the

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LFA. In the pre-Directive scenarios the minimization of the consumption of NR-RM requires an

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increase in the LFA and vice versa, whereas in the post-Directive scenarios the model responds

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similarly to the minimization of the NR-RM and the LFA. These results demonstrate that it is

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pertinent to use the multi-objective optimization technique to solve this problem.

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Figure 3. Normalized results for the minimization of the objective functions

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Figure 4 shows the combination of technologies required for the minimization of the objective

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functions in all the scenarios, as well as the flows of solid OW processed by each of them. The

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flows of processed OW are lower for scenarios with low SSRs because part of the OW present

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in the mixed waste ends up in the inorganic waste stream after the trommel separation

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required for the pretreatment of mixed waste.

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Figure 4. Mass flows of OW to each unit process and performance indicators

364 365

The CF, LFA and consumption of NR-RM of each unit process can be found in Appendix C. The

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ranking of the unit processes according to their CF agrees with the results found in the

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literature for OW,49-52 although the specific values of their CFs differ among publications, given 13 ACS Paragon Plus Environment


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that they are highly dependent on the assumptions made and the waste composition.53

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Regarding the management of the liquid digestate, the ammonia stripping and absorption unit

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was selected as the best alternative to minimize the CF of the system.

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As Figure 4 shows, the shift from pre-Directive to post-Directive scenarios is mostly reflected

373

on the fact that, since the mix-OW cannot be composted, it is incinerated instead. Figure 4 also

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depicts the performance indicators of the studied scenarios. Since the production of fertilizers

375

is very energy intensive, the system configuration that minimizes its CF achieves the highest N

376

recovery rates, which leads to a decrease in the reliance on industrial fertilizers.

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The scenarios with the lowest N recovery rates, which rely on incineration to a greater extent,

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minimize the consumption of NR-RM because of the consumption of NR-RM that is avoided as

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a result of the electricity from the grid mix that is assumed to be displaced.

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The N recovery and the η increase as the SSR increases, although these parameters are not

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directly proportional. As the simplified N flow analysis illustrated in Figure 5 proves, the

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scenario with the highest N recovery is not necessarily the scenario with the highest η; i.e., the

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N losses throughout the system may be larger for the scenario with the highest N recovery.

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This happens because, as noted by Yoshida et al.,54 it is easier for crops to absorb N from

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fertilizers than from the products derived from OW.

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Multi-objective optimization

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The Pareto optimal solutions for each studied scenario are shown in Figure 6. Each Pareto

392

point corresponds to a given system configuration (compiled in Appendix F). The system

393

configurations corresponding to the points with the minimal values of the objective functions

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are those depicted in Figure 4.

395 396

It can be seen in Figure 6 that, as the SSRs increase, the range of values of the objective

397

functions increases too; i.e., the minimal values of the objective functions decrease as the SSRs

398

increase, but this improvement is accomplished increasing the values of the other objective

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functions associated to those Pareto points.

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Figure 5. N flow analysis of different scenarios

404 405

The consumption of NR-RM is lower in the post-Directive scenarios because the fraction of OW

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that is incinerated is larger than in the pre-Directive scenarios, and thus, the avoided

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consumption of NR-RM, is also larger.

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The worse performance of the post-Directive scenarios in terms of the values of the CF and the

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LFA can be attributed to the fewer possible system configurations available in comparison to

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the pre-Directive scenarios, because of the additional restrictions of the model.

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Figure 6. Pareto optimal solutions

416 417

16


Page 17 of 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


418

Research relevance and shortcomings

419

This research demonstrates that the proposed methodological approach provides a valuable

420

framework for the consideration of circularity and sustainability criteria in the design of

421

CIWMSs. Furthermore, it provides a basis to further investigate the consequences of nutrient

422

looping.

423 424

Multiple optimal system configurations for the management of OW in Cantabria were

425

presented; it is up to the regional decision-makers to weight the importance of the identified

426

objective functions and select the desired range of operation values. Although the retrofit of

427

the existing Cantabrian facilities is essential to abide by the current legislation, it is imperative

428

that future work includes an economic evaluation and an assessment of the uncertainty of the

429

results. Furthermore, other waste fractions should be integrated within the developed model

430

so that the restrictions related to the capacity of the unit processes that are not exclusive of

431

OW can be taken into account. Alternative system configurations that contemplate new

432

applications for bio-stabilized materials are also worth exploring.

433 434

Beyond the applicability of the results to solve a real problem, the interest of the research

435

resides in the conclusions about the connection between the circularity of resources and other

436

sustainability aspects that can be drawn. The complete circularity of the nutrient flows within

437

any CIWMS is infeasible, because it does not only depend on the efficiency of the recovery

438

technologies, but also on the ability of plants to capture nutrients. Since crops absorb N from

439

fertilizers more efficiently than from the products recovered from OW, a system configuration

440

with a high N circularity might have larger N losses (and consequently, higher eutrophication

441

impacts) than a system that consumes more industrial fertilizers.

442 443

Moreover, in this case study the minimization of the consumption of the NR-RM leads to the

444

system configuration with the lowest N recovery rates. Hence, this work proves that closing

445

the material loops to a greater extent does not necessarily go hand in hand with a decrease in

446

the overall consumption of resources or the emission of environmental burdens; such claims

447

must be supported by a thorough analysis.

448 449 450 451 17 ACS Paragon Plus Environment


452

Page 18 of 25

NOMENCLATURE

ƞ

N uptake efficiency

C

Carbon

CF

Carbon footprint

CIWMS

Circular Integrated Waste Management System

i

Set of indicators

j

Set of unit processes

LCA

Life Cycle Assessment

LFA

Landfill area

MFA

Material Flow Analysis

mix-OW

Organic waste recovered from mixed waste

N

Nitrogen

NR-RM

Non-renewable raw materials

OFi

Values of the objective functions

OW

Organic waste

P

Phosphorus

Sij

Indicators of each unit process

SS-OW

Source separated organic waste

SSR

Source separation rate

Wj

Amount of waste that each unit process handles

453 454 455

SUPPORTING INFORMATION

456

Waste composition, model data, problem formulation, LCA and optimization results.

457 458 459

ACKNOWLEDGEMENTS

460

The authors gratefully acknowledge the financial support from the Spanish MECD

461

(FPU15/01771) and MINECO (CTQ2016-76231-C2-1R).

462 463 464 465 466 467 18 ACS Paragon Plus Environment

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627 628 629 630 631 632 633 634



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635 636

“For Table of Contents Use Only”

637 638 639

SYNOPSIS

640

Management of organic waste within a circular system aiming at minimizing the consumption

641

of natural resources and the carbon footprint.


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SYNOPSIS Management of organic waste within a circular system aiming at minimizing the consumption of natural resources and the carbon footprint.


Minimization of Resource Consumption and Carbon Footprint of a

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