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An insight-based approach for the design of integrated local food-energy-water systems Melissa Yuling Leung Pah Hang, Elias Martinez-Hernandez, Matthew Leach, and Aidong Yang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b00867 • Publication Date (Web): 07 Jul 2017 Downloaded from http://pubs.acs.org on July 12, 2017

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Environmental Science & Technology

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An insight-based approach for the design of

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integrated local food-energy-water systems

3

Melissa Yuling Leung Pah Hangǂ, Elias Martinez-Hernandez#, Matthew Leachǂ, Aidong

4

Yang§,*

5

ǂ

6

#

7

§

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KEYWORDS. Local production system, insight-based design, resource consumption, exergy,

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food-energy-water nexus

Centre for Environment and Sustainability, University of Surrey, Guildford, GU2 7XH, UK Department of Chemical Engineering, University of Bath, Bath, BA2 7AY, UK Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK

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Graphical Abstract

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ABSTRACT. Society currently relies heavily on centralized production and large scale

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distribution infrastructures to meet growing demands for goods and services, which causes

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socioeconomic and environmental issues, particularly unsustainable resource supply.

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Considering local production systems as a more sustainable alternative, this paper presents an

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insight-based approach to the integrated design of local systems providing food, energy and

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water to meet local demands. The approach offers a new hierarchical and iterative decision

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and analysis procedure incorporating design principles and ability to examine design

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decisions, both in synthesis of individual yet interconnected subsystems and integrated design

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of resource reuse across the entire system. The approach was applied to a case study on

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design of food-energy-water system for a locale in the UK; resulting in a design which

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significantly reduced resource consumption compared to importing goods from centralized

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production. The design process produced insights into the impact of one decision on other

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parts of the problem, either within or across different subsystems. The result was also

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compared to the mathematical programming approach for whole system optimization from

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previous work. It was demonstrated that the new approach could produce a comparable

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design while offering more valuable insights for decision makers.

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1. Introduction

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In today’s economy, centralized production and large scale distribution infrastructures

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provide the main mechanisms for meeting the demands for goods and services in fast

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developing economies. On the other hand, they are also causing problems such as uneven

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economic development, unsustainable resource consumption, and detrimental impacts on

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ecosystems1. In the search for solutions, local production systems have been advocated as a

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possible sustainable alternative to the current centralized and globalized systems2, 3. A local

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production system can be defined as a local network of geographically co-located

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heterogeneous processes (e.g. agricultural, industrial), integrated in a synergistic manner to

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achieve a high degree of resource efficiency, potentially leading to improved economic

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viability while preserving the ecosystem4, 5. The geographical boundary of a local production

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system can be considered as that of an area under the direct governance of a local planning

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body. These systems offer the possibility to increase the use of renewable resources, avoid

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large transportation distances and allow customized system design based on local settings.


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In this work, we are particularly interested in local systems for the provision of food, energy

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and water. Recognized by the term “food-energy-water nexus”, the interconnectedness

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between food, energy and water subsystems has recently been widely acknowledged at the

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regional, national and global scales6, 7, 8. Though these three sectors are inextricably linked,

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these areas have too often been considered in isolation6. Thus, it is highly desirable to

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develop tools for analysis and design that consider their interdependencies9. To-date, a range

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of methods and tools has been developed for investigating the interconnected food, energy

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and water systems, including those for modeling and assessment10, 11, 12 and those for optimal

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design and planning13,14,15. Most of the existing work however addresses larger (e.g. national

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and regional) scales. At the local scale, the design problem for a food-energy-water system

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can be defined as to determine the combination of processes which can meet a set of demands

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by a population in a locality within the availability of local resources so that total resource

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consumption is minimized while observing a set of technical, environmental and ecological

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constraints. As the local scale offers geographical proximity between different processes,

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optimization can not only consider general inter-dependencies between the food, energy and

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water systems, as is typically done by work on larger scales, but also explore symbiotic

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resource (e.g. heat, organic waste) reuse opportunities between specific facilities that can

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realistically be physically connected only at the local scale.

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Our previous work5 has developed a mathematical programming (MP) based approach for

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designing local integrated production systems (LIPS) with a particular application to food,

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energy and water. A main limitation of this approach is that the solution process is entirely

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one of mathematically solving an optimization model: although a decision maker can be

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involved in constructing the model, what is presented to him or her is merely the final design,

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which makes it difficult to understand the impact of different factors, options, inter-

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subsystem interactions and trade-offs.



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In this current work, an alternative, insight-based approach is developed. In the literature of

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process design and integration, the term “insight-based” typically refers to approaches based

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on pinch analysis which are in contrast with MP-based approaches16,

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analysis techniques encompass resource cascading and recovery based on thermodynamic

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principles, and have previously been applied to design systems involving heat18, water19 and

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hydrogen20 exchange, to name a few examples. More recently, pinch analysis has been

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applied to the concept of a locally integrated energy sector21, 22 where heat and renewable

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energy sources are integrated and exchanged between diverse industrial processes.

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. Broadly, pinch

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In this work, the term “insight-based” is used with a broadened meaning: still in contrast with

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an approach purely based on mathematical programming which solves the design problem by

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using a monolithic optimization model, the insight-based approach aims to provide an

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incremental decision and analysis procedure incorporating design principles, which include

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pinch analysis techniques and design rules based on metrics measuring resource efficiencies

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to suggest and examine design alternatives. Such a decision procedure will produce insights

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to the decision makers such as the impact of choosing one option on the rest of the system

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and the comparative performance between different options. More importantly, with MP-

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based approaches, any insights are generated only at the end of the decision-modeling

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process ; in contrast, the incremental procedure (detailed in Section 2) would allow the

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decision makers to gain such insights at a series of intermediate steps, where they can also

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scrutinize, approve or reject the “optimal” options identified from the suggested design

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principles, possibly with considerations not explicitly covered by the design principles, and

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provide further information such as new design options and adapted parameter values that

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can be incorporated in the subsequent stages of the design process.

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Although the insight-based approach is proposed as an alternative to the MP-based approach

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and highlights the use of design principles as opposed to optimization models and

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sophisticated algorithms, its step-by-step nature does not exclude, and in fact can readily

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incorporate, the use of MP to solve a sub-problem at a certain step, as demonstrated in the

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case study (Section 3.1.3). The flexibility of the approach also lies in the capability for

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supporting both new-design and re-design tasks. In fact, most of its practical applications will

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be for improving an existing local food-energy-water system. Using a generic concept of

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resource gain (Section 2.1), a new design option can be compared either with other new

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options or with the existing system. Although the latter case requires to consider both (i) the

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potential operational gain due to the new option and (ii) the cost for replacing the existing

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system with the new option (i.e. the retrofitting cost) (Section 2.3), the overall procedure

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applies equally well to both cases.

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In the rest of the paper, we present the insight-based approach to the design of local

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integrated production systems (LIPS) for food, energy and water. This approach covers both

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(i) the generation of the basic design of individual subsystems taking into consideration their

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interactions and (ii) cross-subsystem integration to maximize the whole-system resource

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efficiency. Its application is demonstrated through a case study on an eco-town in the UK.

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2. Methodology

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The insight-based approach for LIPS design, as illustrated in Figure 1, consists of two main

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stages, namely synthesis (Sections 2.3 and 2.4) and integration (Section 2.5), guided by a

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Locally Integrated Production System Onion Model (LIPSOM) (Section 2.2).



The integration stage: resource cascading, regeneration and re-purposing (2.5/3.1.5) Re-assess amount of fresh resource required

Specify demands and resource availability The synthesis stage: generation of a basic design Assume default energy/water supply (2.4.3/3.1.1)

Design food subsystem (2.3/3.1.1)

water requirement

energy requirement

Design water subsystem (2.3/3.1.2)

Design energy subsystem (2.3/3.1.3)

energy requirement

Base design converged?

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Use new solution for water and energy supply parameters; update shared resource availability (2.4/3.1.4)

Cascade by pinch analysis & design

Identify the regeneration and repurposing options with RG and RG

No

No Yes

Yes

Use regeneration option

Any resources for direct reuse?

Use repurposing option Yes

Is ? >

No

No All and negative? Yes Design treatment for discharge

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Figure 1. Design procedure for the insight-based approach. In the brackets are the

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corresponding section numbers in the methodology/case study parts of the paper.

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The overall approach starts with the synthesis stage, to produce a “base” design of LIPS

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based on (i) principles for designing individual subsystems (Section 2.3) and (ii) a sequential

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synthesis procedure for converging the design of subsystems into that of the entire LIPS

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(Section 2.4). In the subsequent integration stage, options for cascading, regeneration and re-

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purposing of resource streams, which are not considered at the synthesis stage, are explored,

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the consequence of which may require revising the “base” design. Design decisions in both

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stages make use of the concept of “resource gain” (Section 2.1), to eventually achieve the

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goal of minimizing total resource consumption.

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2.1 Design goal and resource gain

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As in our earlier work5, the design goal is to minimize the total cumulative exergy

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consumption (CExC) of the LIPS in meeting the local demands and while satisfying resource

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and ecological constraints. CExC is defined as the sum of exergy of all resources consumed

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along the supply chain of a product/service23. In the literature of resource accounting, CExC

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and several other exergy-based approaches have been proposed to offer a unified and

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thermodynamically rigorous quantification of resource requirements and environmental

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impacts24,

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material and energy resources not only as inputs to the various stages leading to the supply of

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products and services, but also including the resource consumption for environmental

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remediation to tackle waste streams and emissions. However, it does not effectively cover a

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wide range of social and economic factors, for which additional tools are needed along with

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the approach proposed here.

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As a key aspect of considering a LIPS is to assess its comparative performance against

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conventional options, e.g. satisfying local demands with external imports from centralized

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production, a resource gain (RG) indicator is introduced, which is defined as the net avoided

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CExC due to substitution of a reference (often conventional) option by a different, alternative

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option to be evaluated:

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25, 26, 27

. In this work, CExC is used to capture the cumulative consumption of

= −

(1)

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and are the CExC by adopting the reference option and the alternative

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option, respectively. The nature of a design option being assessed depends on that of the

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corresponding decision to be made. The key types of decision are listed in Table 1, with an



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example of each type and the corresponding metric for making the decision. The metric is

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either RG as defined in Equation (1), or one that is derived from this RG.

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Table 1: Type of decision tasks and metrics for decision making. Decision task Producing locally or importing

Examples Decision between producing bread locally or importing bread to satisfy local bread demands

Metrics RG as defined in Equation (1)

Selection of design technology/process options: new design

Decision between wind or solar for energy supply-

RG as defined in Equation (1)

Selection of design technology/process options: retrofitting design

Decision between introducing a wind power facility and continuing the use of grid electricity

Payback period of new capital resources, as defined in equation (3), Section 2.3

Selection of resource for a production process

Local or imported; fresh or regenerated

Resource allocation between competing uses

Land for food and/or energy crops

RG for regeneration options as defined in equation (4) , Section 2.5 Specific Resource Gain (SPR) as defined in Equation (2) below.

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In particular, the last decision task in Table 1 requires the use of a slightly adapted concept,

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namely specific resource gain (SRG). A resource as provided by the local environment may

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serve multiple competing purposes and satisfy various demands. For example, crop residues

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as a resource, can either be used for animal feed or energy generation. Choosing one or

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another purpose may result in lower or greater resource consumption. In order to decide

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which purpose should be satisfied with priority, SRG is defined in Equation (2) as resource

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gain per resource quantity allocated to a particular purpose:

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+ = ( − )/-

(2)

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where - is the resource quantity allocated. Between two alternative purposes, the one leading

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to a higher SRG should be given a higher priority in resource allocation due to the higher

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level of resource savings (measured by CExC) that can result from serving this purpose. As

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such, SRG for fulfilling a certain purpose in connection with a particular resource allows this 8 ACS Paragon Plus Environment

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purpose to be gauged with other competing ones, to support resource prioritization which

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may take place within (see Section 2.3) or between (see Section 2.4.2) subsystems.

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2.2 LIPSOM: Locally Integrated Production System Onion Model

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Design activities in the proposed insight-based approach are organized with the guidance of

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the LIPSOM, as shown in Figure 2.

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Ecological

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Agricultural

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Industrial

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Resource cascading

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Environmental remediation

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Figure 2: Locally Integrated Production System Onion Model (LIPSOM).

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LIPSOM is based on the onion model developed by Douglas28 as a conceptual design

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approach for process synthesis, in which a hierarchical procedure is followed starting from a

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reactor and gradually expanding the system boundaries as successive levels are added. The

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onion model highlights the sequence of design steps where each layer involves making

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design decisions that will affect those to be made in the successive layers of the model. As an

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adaptation to the original onion model, LIPSOM consists of five sequential layers and aims at

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capturing important interdependencies between them. The model starts with the ecological

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layer, to develop considerations on the local ecosystem consisting of components such as

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water bodies, atmosphere and land. The main purpose of design considerations at this layer is 9 ACS Paragon Plus Environment


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gathering ecological information such as the resources available and their constraints (e.g.

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biomass yield, land availability). This will offer important inputs to the subsequent design

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particularly with respect to resource selection and allocation, and identification of ecosystem

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components that could be later interconnected to the production processes (e.g. wetland for

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wastewater treatment). Next, the agricultural layer addresses processes for producing food

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and non-food crops and livestock. In this layer, one can establish what agricultural processes

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can take place in the locale of concern, design options for each process, and the resource cost

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for each option. Locally available resources identified from the ecological and agricultural

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layers can be classified into: a) basic resources from existing ecosystems and the

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environment such as solar radiation, wind, forest biomass, that are unprocessed and

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unmanaged; and b) managed resources such as cultivated biomass, food crops and livestock.

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As suggested earlier27, the cost of the basic resources is simply measured by their exergy

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content, while that of the managed resources is quantified by the cumulated exergy

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consumption across all the steps and activities for producing the resources.

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The next layer is the industrial layer where industrial (including municipal) processing units

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are considered, such as those for food processing (e.g. bread production), energy conversion

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(e.g. solar panels), and water processing (for clean water supply or wastewater treatment).

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Process options, judged feasible based on the resource availability identified in the ecological

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and agricultural layers, will be identified and their resource consumption evaluated.

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Combined with the learning from the agricultural layer, this will enable the base design of

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subsystems, producing a final product to meet local demand (e.g. electricity or bread

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production); the principles for such design are outlined in Section 2.3.

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In the resource cascading layer, any used or residual resources generated from the agricultural

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and industrial production processes (e.g. wastewater), which have not been fully utilized in 10 ACS Paragon Plus Environment

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the basic design, are considered for further utilization. As the process integration part of the

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insight-based approach, this layer takes into consideration all the process integration

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opportunities within and across the subsystems, including options for regeneration and re-

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purposing of resources.

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The last layer comprises the design of environmental remediation units where either a

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technological or an ecological (e.g. wetland) option can be considered for the treatment of

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each of the resources identified from the cascading layer that cannot be further used for any

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purpose.

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In connection with the two design stages introduced earlier with Figure 1, the ecological layer

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provides important information for the synthesis stage, where the base design of subsystems

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(food, energy, water) is carried out at the agriculture layer and the industrial layer. The

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resource cascading layer corresponds to the integration stage where further optimization can

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be applied. The final layer, environmental remediation, is considered in both stages when

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either a base design option or an integration option leads to discharges into the environment.

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In such a case, the cost for environmental remediation needs to be included in calculating the

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resource gain of the design option in question. The connection between LIPSOM and the

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overall design procedure is illustrated by the graphical abstract. 2.3 Principles for designing individual subsystems

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A single subsystem of LIPS may involve components from ecological, agricultural and

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industrial layers. For example, a food subsystem could make use of local land and rain water

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to grow crops which are further processed into food products. Typically, the design of such a

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subsystem needs to incorporate considerations pertaining to all these layers, following several

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steps:



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1. Identify the availability of local and external resources.

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2. Identify technical options for agricultural or industrial processes required.

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3. Determine resource implications for each resource or technical option.

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4. Establish a reference system comprising conventional options, against which alternative, potentially more advantageous options can be compared.

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5. Determine the design by choosing advantageous options based on resource gains.

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Several details are given below for the final decision making step.

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Firstly, at this step, RG is typically calculated for each alternative option, according to

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Equation (1), and an option with a positive RG, or one with the greatest positive RG when

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multiple alternatives exist, will be adopted. In some special cases where a retrofitting

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decision is to be made on whether a new and operationally more efficient technical

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component (e.g. an anaerobic digestion reactor for energy production) should be introduced,

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the operational RG, calculated as operational gain per unit time (e.g. per year) without

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considering resource consumption needed for building and installing the new component -

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referred to as capital resources - can be first calculated. Next, the period of time for the

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payback of capital resources (T) is determined as

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T=

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with CExC7 denoting the CExC for capital resources associated with the new facility in

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question. This payback period, expressed in the same time unit as for calculating the

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operational gain, can then be compared with the decision maker’s expectation or the service

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life span of the component, to determine its favorability.

./.012

(3)

34


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Secondly, if multiple purposes (e.g. in agriculture, local production of bread or pork) within

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the subsystem compete for a limited resource (e.g. agricultural land), resource allocation will

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be based on the SRG (introduced in Section 2.1) of each of these purposes. In principle, the

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resource is to be used first to satisfy the purpose with the highest SRG, until the demand for

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that purpose is met either fully or to the extent possible. If spare resource is still available,

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remaining purposes are satisfied in the descending order of SRG.

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As the third point, the design needs to ensure that all the ecological limits identified in the

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ecological layer are met. In particular, the selection of a resource flow i needs to satisfy

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78 9 8 :8

< 1, where cA is the current (existing) consumption, xA is the (additional) quantity of

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the resource consumed for the design of the subsystem and yA is the maximum quantity of the

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resource available locally according to either the natural replenishment rate or any authorized

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constraint (e.g. water abstraction limit set by water authorities). As another example, applying

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this condition to the ecological output of heathland biomass would mean that cA is the current

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biomass consumption in the locale, xA is the additional consumption for the design under

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consideration and yA is the natural growth or replenishment rate of the biomass.

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As a final remark, the above rules and principles could be sufficient for “manually” designing

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a subsystem where not too many options are to be assessed. However, when the number of

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options increases to a level which makes a manual design prohibitively complex, a

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mathematical programming problem could be formulated at the subsystem level to minimize

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total CExC of the subsystem while observing resource and ecological constraints as stated

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above.

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2.4 Sequential synthesis of multiple subsystems



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Beyond the design of individual subsystems, much of the complexity of synthesizing a whole

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LIPS lies in handling the connections and interactions between subsystems. This involves the

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determination of the sequence for designing the individual subsystems and the handling of

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inter-subsystem stream connections and resource allocation. In this section, principles for

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these tasks and an iterative design procedure for the complete process synthesis are presented.

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2.4.1 Synthesis sequence

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When designing a LIPS that involves multiple subsystems, one needs to decide the sequence

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in which subsystems should be considered to make the design process more efficient, by

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reducing the need for design iterations caused by subsystems’ interconnections. As a general

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principle, the place of a subsystem in a design “queue” should be in accordance with the

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degree to which its design is affected by the design decisions of the other subsystems. The

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design of a subsystem typically depends on the following factors which are affected by the

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inter-subsystem connections:

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(1) Availability of required resources

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(2) Cost of required resources

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(3) Product demand

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If a subsystem requires a resource from another subsystem, then (1) and (2) could be affected

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by the latter subsystem via its capacity and efficiency respectively. If a resource is to be

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shared by other subsystems, (1) will be affected due to inter-subsystem competition. If the

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output of the current subsystem is input to another subsystem, (3) will be affected. For a

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food-energy-water system, each of the three subsystems involves interconnections affecting

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(1) and (2). In comparison, the food subsystem in most cases is simpler because its demand

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(i.e. (3)) is generally not affected by the other two subsystems. As such, it could be more

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independent, and therefore chosen to be designed first. However, the order between the two 14 ACS Paragon Plus Environment

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other subsystems is rather difficult to determine generally, and should be decided on a case-

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by-case basis. In the following, the design approach is explained assuming the water

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subsystem is more independent than the energy subsystem and hence should be designed

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first. It should be noted that the degree of independency of each subsystem may change,

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depending on the context. For example, if in a particular system the energy generation

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happens to heavily depend on the waste streams from food production, theoretically the

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required output from the food subsystem may be affected by the energy subsystem, deviating

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from the more common case described above. In this and other situations where there is no

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subsystem which is significantly more independent than the others, one may randomly

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choose a subsystem as the starting point of the iterative design process. Note that the

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sequence of designing the subsystems affects the effort needed for reaching a convergence; it

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is not expected to change the converged design outcome.

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2.4.2 Inter-subsystem resource allocation

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In Section 2.3, a principle was presented for allocating a shared resource between multiple

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purposes in a single subsystem. If a resource is potentially shared by multiple subsystems, its

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availability to each subsystem needs to be updated when moving from the design of one

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subsystem to another. An example of this consideration is land allocation between growing

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food crops and energy crops. When the food subsystem is designed prior to the energy

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subsystem, all the agricultural land is considered as available for food production. When the

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energy subsystem is designed in a later step, if a technical component consuming energy

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crops (e.g. biomass Combined Heat and Power, CHP) is identified as a preferred option, and

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there is no sufficient land available for supplying energy crops due to the occupation by the

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food subsystem, its SRG needs to be compared with those of the options chosen in the food

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subsystem design. If the former turns out to be higher, land will be re-allocated from the food 15 ACS Paragon Plus Environment


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subsystem to the energy subsystem; and the former will be re-designed (in the next iteration,

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see Section 2.4.3) based on the reduced land availability.

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2.4.3 A sequential synthesis procedure

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Implementing a determined design sequence and handling inter-subsystem connections, a

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sequential iterative procedure for synthesizing a complete LIPS is proposed.

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At the start of the procedure, synthesis will assume conventional utilities, without considering

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alternative sources that the local system can potentially offer. For example, the food

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subsystem uses grid electricity (as opposed to power from local renewables) and standard

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ground water (as opposed to rain water), as the cost information of these alternatives is yet to

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be produced later when the energy and water subsystems are designed. Following the design

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sequence determined in Section 2.4.1, the food subsystem is first synthesized, using the

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principles stated in Section 2.3, in isolation from the other subsystems. Then, the water

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subsystem is synthesized, but considering the water demands of the food subsystem just

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designed in addition to the other local water sinks (e.g. residential users). Alternative water

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streams that can be used by the food subsystem and the other local water uses and their

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associated CExC will be determined, and preferable options chosen. Lastly, the energy

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subsystem is synthesized taking into account the energy sources and sinks arising from the

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initial design of the food and water subsystems and other local energy demands, with

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examination of energy streams that can be generated locally or obtained from centralized

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supply and selections made.

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Following the initial pass of design, the first iteration is carried out. The water and energy

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supply parameters from the initial pass are used and the same design sequence repeated. This

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particularly includes replacing any assumptions of using conventional utilities as adopted at 16 ACS Paragon Plus Environment

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the start of the design process with the energy and water supplies concluded from the initial

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pass of design. The iterative process stops when the quantity of resource streams (e.g.

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rainwater, solar power) exchanged between the three subsystems and their corresponding

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specific CExC become stable, and no further adjustment is needed for inter-subsystem land

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allocation.

357 358 359

2.5 The integration stage: resource cascading, recycling and regeneration

360

Following the synthesis stage, resource cascading can be carried out upon the synthesized

361

base system. Cascading has often been applied in the context of resource scarcity as a

362

resource conservation technique29. In this work, it refers to the multiple re-use of a resource

363

stream, resulting in quality degradation in each instance of use, in absence of quality up-

364

lifting between uses. Two cases of cascading use of a resource can be identified:

365

(1) Same-purpose cascading use where the successive uses are based on the same quality

366

of the resource (e.g. COD level) for that purpose, e.g. reusing water to satisfy water

367

demands at different COD levels.

368

(2) Re-purposing where the next use of the resource is based on a quality different from

369

that of its previous use. For example, repurposing might involve processing

370

wastewater for nutrient recovery rather than for water supply.

371

A high quality resource is usually either expensive to produce or scarce. Therefore, the design

372

of a series of cascading use of a certain resource generally aims to minimize resource costs by

373

matching the quality “grades” of the resource with the levels of demand for quality. In fact,

374

this is the common principle shared by the existing pinch analysis and design approaches18.

375

These approaches are adopted for designing the cascading use of resources in LIPS. 17 ACS Paragon Plus Environment


Page 18 of 33

376

At the end of a series of cascade steps, reprocessing could be undertaken to allow resources

377

that would otherwise be wasted to be used again through the same process or through

378

different type of processes. Re-processing may materialize in (i) upgrading or regeneration to

379

improve the quality of a used resource to enable recycling, i.e. reuse of the resource in the

380

same application, or (ii) any treatment needed to repurpose the resource. Options for

381

recycling or repurposing are ranked according to their RG, evaluated by adapting equation

382

(1):

= CDEFF + H − (4)

383

384

where,

385

CDEFF is the CExC of fresh or other resources avoided by recycling or repurposing;

386

H is the CExC for treating waste that would arise from the used resource if it

387

was not reused through recycling or repurposing;

388

is the CExC needed by the reprocessing to prepare for recycling or repurposing.

389

For each used resource considered, the recycling or repurposing option with the highest RG is

390

adopted.

391

At the end of the recycling/re-purposing analysis, any excess unused resources that are not

392

worth being further utilized will have to be treated before being discharged into the

393

environment. If different environmental remediation options exist for treating the unused

394

streams, determine the CExC for each option and choose the one with the lowest HC ,

395

EH CExCHC .

396 397

3. Results and discussion


Page 19 of 33


398

This section presents a case study to demonstrate the application of the insight-based

399

approach for designing a local integrated food-energy-water system, through the following

400

aspects:

401

•

subsystems, and a linear programming assisted design of the energy subsystem.

402 403

Using RG based design principles to generate the base design of the water and food

•

Using the sequential design procedure to produce a complete whole-system base design.

404 405

•

Cascading use of wastewater and waste heat within and across subsystems.

406

•

Assessing regeneration options for wastewater.

407

To simplify the case study, regeneration for waste heat source and re-purposing options

408

for both wastewater and waste heat were not included. As no retrofitting decision was

409

considered, the design principles based on the payback of capital resources were not

410

demonstrated. Section 3.1 presents the main results from the various stages of the case study;

411

all the detailed results, calculations and assumptions can be found in Supporting Information.

412

In Section 3.2, comparisons are made between the resource consumption of the LIPS

413

designed by the insight-based approach and that of a system relying on external, centralized

414

supply and between the results of the insight-based approach presented in this work and those

415

of the mathematical programming approach from our earlier work, to offer an overall

416

assessment.

417

3.1 Application of the insight-based approach

418

The selected case study locale is the town of Whitehill and Bordon in southeast England,

419

being regenerated into a sustainable green town; its ecological and climatic settings and

420

demand data are obtained from the local development agency30 and from central 19 ACS Paragon Plus Environment


Page 20 of 33

421

government31,

422

considered, by designing the subsystems separately for the four seasons, and considering

423

cross-season storage for crops and rainwater. Throughout the case study, a number of

424

quantitative results will be presented, which have been obtained based on a specific set of

425

parameter values mostly adopted from literature. In reality, there are inevitably issues arising

426

from data quality and uncertainties, which may impact on the reliability of individual results

427

and consequent decision recommendations. There are established approaches to deal with

428

these issues, such as sensitivity analysis and optimization with uncertainties, and these have

429

in fact been applied along with the work on the optimal design of local food-energy-water

430

systems33, which however are not included here to allow this paper to focus on the main

431

design approach.

432

32

. Seasonal variations in both resource availability and demands are

3.1.1 Initial design of the food subsystem

433

The food products considered are bread, potatoes, pork and beef and have been chosen based

434

on local food preferences in the eco-town. These food choices also give a good representation

435

of a human being’s dietary requirements in carbohydrate, protein and fats. With a total

436

availability of 17 ha, agricultural land is a limited resource and as such the specific resource

437

gain + of each food type, with the imported food as the reference option, was determined

438

using Equation (2) (see Section 2.1 for the rationale behind the use of SRG). The food

439

products with + values in decreasing order were bread, potatoes, beef and pork, at

440

3.17×104 MJ/ha, 9.29×103 MJ/ha, 6.02×103 MJ/ha, 1.02×103 MJ/ha respectively, resulting

441

from the difference in the CExC value between the imported and the locally produced

442

products, as well as the local yield, of each food type. Using the design principle for resource

443

allocation, growing wheat for bread was given priority to receive land, and it turned out that

444

all the land was to be allocated for this purpose and 60% of the bread demand could be


Page 21 of 33


445

satisfied by local wheat; while all other food demands needed to be imported due to limited

446

agricultural land availability, despite their positive RG.

447

3.1.2 Initial design of the water subsystem

448

The initial design of the water subsystem needed to satisfy the water demand by bread

449

manufacture from the food subsystem and the residential sector, with groundwater and

450

rainwater as the sources available. The potential uses of rainwater at this stage were limited to

451

wheat cultivation and non-potable domestic water uses. Using Equation (1) and taking

452

groundwater as the reference resource, the RG for rainwater was determined to be -0.048

453

MJ/kg. The negative RG was due to the relatively high capital resources to implement a local

454

rainwater harvesting system leading to groundwater being overall a more resource efficient

455

alternative for water supply. Groundwater has an abstraction limit in the eco-town; however

456

this is sufficiently high25 and therefore groundwater use was not limited by its availability.

457

3.1.3 Initial design of the energy subsystem

458

Alternative electricity generation sources from wood chip biomass CHP, organic waste CHP,

459

natural gas CHP, solar and wind and heat generation from wood chip biomass boiler, wood

460

chip biomass CHP, organic waste CHP and natural gas CHP were considered alongside grid

461

electricity and heat from natural gas boilers as conventional energy sources. Agricultural

462

residues produced from the initial design of the food subsystem are available in summer for

463

energy production, which is assumed to be able to merge with organic waste from municipal

464

operations to feed into a CHP facility. As using the RG based principles to design this

465

subsystem manually would be too complex given the number of options to be considered,

466

linear programming (LP) was adopted to generate a fast optimum design. The objective

467

function of the LP problem was to minimize the net total CExC of this subsystem while 21 ACS Paragon Plus Environment


Page 22 of 33

468

meeting local heat and electricity demands, allowing the generation of surplus electricity if a

469

CHP facility was selected.

470

When the difference between the CExC of locally generated surplus electricity and the CExC

471

of grid electricity was taken as resource credit in the objective function, the optimal design of

472

the energy subsystem was to produce most of its electricity output from wood chip biomass

473

CHP (49.6%), followed by wind power (19.9%), solar (17%) and organic waste CHP

474

(13.5%). Reflecting the resource credit assigned to the exported electricity, virtually all the

475

power generated by CHP was surplus. The total heat demand for the eco-town was met fully

476

by the wood chip biomass CHP (87%) and organic waste CHP (13%). This electricity mix

477

corresponds to an average specific CExC of 1.90 MJ/MJ electricity; a significant 68%

478

decrease from grid electricity. The average specific CExC of supplying heat was determined

479

to be about 1.80MJ/MJ heat; which corresponds to a 10% decrease from that of supplying

480

heat from conventional natural gas boilers. Furthermore, taking agricultural residues as feed

481

for energy production resulted in a reduction of the total CExC required by 4% compared to

482

not considering the residues.

483

If the resource credit of producing surplus power was not included in the objective function

484

of the LP energy model, the energy supply for satisfying heat demand includes 13% organic

485

waste CHP, 9.5% biomass CHP and 77.6% from wood chip biomass boiler while the

486

electricity mix would comprise 50% wind, 14% wood chip biomass CHP and 36% organic

487

waste CHP with no surplus electricity generated. The average CExC was determined to be

488

540,150 GJ/y compared to the average CExC of -41,361 GJ/y in the initial energy production

489

subsystem; indicating that the inclusion of a credit for avoiding the CExC associated with

490

grid electricity through local electricity export decreases resource consumption significantly;


Page 23 of 33


491

far offsetting the amount of resources spent to produce energy locally. The use of agricultural

492

residues did not have any appreciable impact on the total CExC of such an energy system.

3.1.4 Iterative design

493

494

Following the initial pass of the sequential synthesis procedure, further design iterations were

495

carried out. Table 2 shows the outcome of the 1st iteration, along with the insights gained

496

from the change in the design outcome resulting from this iteration. The 2nd iteration was

497

subsequently conducted, which did not alter the selection of design options, but the 15%

498

decrease in energy consumption for groundwater use from the 1st iteration led to the change

499

in the RG ranking of the food products, with beef now being more efficient to produce than

500

potatoes, which suggests that beef production cost is rather sensitive to water supply. Also,

501

although there were changes in the energy demand from the food and water subsystems, these

502

changes were not significant enough to modify the optimal energy mix supplied by the

503

energy subsystem, suggesting the overall design was becoming stable. In fact, the

504

convergence criterion was met following the 3rd iteration, marking the completion of the

505

synthesis stage. The final base design is illustrated in Figure 3.

506

Table 2: Outcome of the 1st iteration. Subsystem

Design outcome

Insights from initial to 1st iterative design

Initial design:

-With similar water but cheaper energy supply

The food products with SRG values in decreasing order 4

cheapest food type to produce locally,

were bread, potatoes, beef and pork, at 3.17×10 3

3

MJ/ha, 9.29×10 MJ/ha, 6.02×10 MJ/ha, 1.02×10 Food

3

indicating that pork is very sensitive to resource cost for energy supply.

MJ/ha respectively. st

-Between beef and pork, the local production

1 iterative design: 4

- Bread still has the highest RG at 4.63×10 MJ/ha 4

followed by pork at 1.16×10 MJ/ha, potatoes at 3

to its initial design, pork becomes the second

3

of the latter would still consume more energy than the former despite the change in energy

9.29×10 MJ/ha and beef at 3.63×10 MJ/ha.

cost per unit (kg), as the quantity of pork

- 10% and 68% decrease in specific CExC for heat and

produced locally based on land available is



electricity respectively.

Page 24 of 33

higher than beef, with a land to pork ratio of 1.2 ha/ton compared to 4.7 ha/ton for beef28.

Initial design:

Water

- Specific CExC groundwater was 0.06 MJ/kg while

- More resource-efficient using groundwater

RG for rainwater was -0.048 MJ/kg.

to satisfy all water demands in the eco-town

1st iterative design:

rather than using rainwater due to its negative

- Specific CExC groundwater was 0.051MJ/kg (a 15%

RG and also the relatively high abstraction

decrease) while RG for rainwater was -0.06 MJ/kg.

limit for groundwater.

- New water demand and wastewater generation from energy production was met. Initial design: - Electricity demand supplied by 52% wind, 45% solar and 3% wood chip biomass CHP.

Energy

- Average specific CExC of 1.89 MJ/MJ electricity

- Contribution of wood chip biomass CHP,

- Heat demand supplied by 87% wood chip biomass

which has a relatively high specific CExC, in

CHP and 13% organic waste CHP with an average

the electricity mix increased by 3.5%

specific CExC of 1.80 MJ/MJ heat

compared to the design from the initial pass

1st iterative design:

due to the need to satisfy higher energy

- Electricity demand supplied by 50.1% wind, 43.3%

demands.

solar and 6.5% wood chip biomass CHP. - Average specific CExC of 2.02 MJ/MJ electricity; 6.5% higher than in initial design. -Same heat source mix as in initial design.

507 508


Page 25 of 33


509 Food production subsystem

510 511

Imported fertiliser

512

Wheat cultivation

Wastewater

Wheat processing

Wheat

Agricultural residues

513 514

Bread

Water production subsystem Water

515

Water

Groundwater treatment

516

Residential

Imported food

517 518

Residential wastewater treatment

519

Discharge

520 521 Electricity 522 523

Wastewater

Food wastewater treatment

Discharge

Energy wastewater treatment

Discharge

524 525

Wastewater Energy production subsystem

Surplus electricity to grid

526 527 528

Solar

Wind

Organic waste CHP

Biomass CHP

529 530 531 532

Heat

Figure 3: Base design of the local production system.

3.1.5 Integration: water reuse and regeneration

533

After the base design was generated, the system was optimized by considering integration

534

options for resource reuse. As all the water sources considered had a COD level much higher

535

than the COD requirements of any water sinks, from a quality perspective the cascade use of

536

the water sources through the application of pinch analysis would result in no possible

537

recovery. As direct re-use was infeasible, options of regeneration (to enable reuse) were

538

evaluated. The RG for regenerating different water sources of residential wastewater, 25 ACS Paragon Plus Environment


Page 26 of 33

539

wastewater from food production, wastewater from energy production up to the desired

540

quality of the water sinks were assessed using Equation (3). Taking into account resource

541

cost for environmental discharge of un-regenerated wastewater and the cost of fresh water

542

avoidable by using regenerated water, the net specific CExC for regeneration was determined

543

to be positive, hence supporting the regeneration option. The use of regenerated wastewater

544

was limited to wheat cultivation, non-potable residential purposes and energy production, but

545

not for food processing, in line with health and safety regulations29. It was determined that

546

about 61% of the eco-town’s water demands could be satisfied by regenerated water sources,

547

hence significantly reducing the consumption of groundwater. The average specific CExC of

548

water supply was reduced by 60% from its original value in base design. The reduction in

549

total CExC of the water subsystem did not impact the design decisions of the energy

550

subsystem as water was not a significant component (less than 1%) of the specific CExC of

551

the energy options. Any remaining unused streams were to be treated before environmental

552

discharge.

553

3.1.6 Integration: energy reuse

554

Pinch analysis30 was applied to optimize the use of heat available in each season. Low

555

temperature (LT) waste heat available from organic waste CHP and wood chip biomass CHP

556

was candidate for reuse to meet heat demands from industrial bread production, wheat

557

storage, wastewater treatment plant and residential. It was determined that adopting 3 heat

558

exchangers placed above the pinch would allow for maximum heat recovery of 2.79×107

559

MJ/y. The reuse of LT waste heat contributes to satisfying about 10% of the total heat

560

demand. The proportion of high and medium temperature heat produced from wood chip and

561

organic waste CHPs in the heat energy supply mix decrease from 87% and 13% to 78.4% and

562

11.7% respectively. With this new heat energy supply mix, the average specific CExC of heat 26 ACS Paragon Plus Environment

Page 27 of 33


563

was reduced by 10% from its value from the base design. However, the new specific CExC

564

for heat coupled with cheaper water supply did not alter the order of the SPG for the different

565

food options, though SPG for pork followed very closely that of bread. Similarly, the design

566

of the water subsystem was not changed.

567

Together, water and energy reuse design showed that the decisions in the integration stage

568

did not lead to a qualitatively different design from the synthesis stage in this particular case.

569

However, the base design was made more efficient, through the reduction in energy

570

production required from the CHPs and the consumption of groundwater.

571

3.2 Comparative analysis and final assessment

572

To show the extent to which a LIPS, designed following the insight-based approach, can

573

achieve resource savings, the results presented in Section 3.1 are compared with the resource

574

consumption for meeting the same local demands by another scenario termed “centralized

575

supply”, which imports food and utilities (grid electricity and natural gas). Figure 4 illustrates

576

the external resource consumption of each subsystem for each scenario, which clearly

577

demonstrates the resource advantages of the LIPS: when including the credit for exported

578

surplus electricity, the LIPS consumes less than 10% of resource (measured in CExC) needed

579

by the centralized supply scenario; a 39% saving is still achieved even without the

580

aforementioned credit.



Cumulative exergy (GJ/y) 2000000 1500000

Page 28 of 33

Net cumulative resource consumption for each scenario Integrated design Centralized supply

1000000 500000 0 Food subsystem -500000

581

Water subsystem Energy subsystem Electricity export Total net resource consumption

-1000000

582

Figure 4: Comparison between the LIPS and the system with centralized supply.

583

The result of the insight-based design approach was also compared to that of the

584

mathematical programming (MP) approach developed earlier5, which was adapted to include

585

all assumptions and options for food, water and energy subsystems considered in this case

586

study. The overall resource consumption of the two designs appeared to be similar, with the

587

result of the MP approach being slightly better. A closer inspection of the design decisions by

588

the MP approach (see SI) revealed that both approaches suggested qualitatively identical

589

designs in terms of the food product and technical options selected for local production.

590

Quantitatively, both designs suggested the same decision for the food subsystem, i.e. using all

591

the land to meet 60% of bread demand. There were minor differences in the percentage of

592

groundwater replacement by regenerated wastewater and in the energy mix proportions, and

593

there was a noticeable (6%) increase in waste heat recovery identified by the MP approach.

594

These quantitative gains by the MP approach are not surprising, given its adoption of

595

rigorous and simultaneous mathematical optimization.

596

Overall, it can thus be inferred that the insight-based approach offers sensible and comparable

597

design solutions. Compared to the purely MP-based approach, the extensive use of design

598

rules (including those of pinch analysis and those based on decision metrics) and the 28 ACS Paragon Plus Environment

Page 29 of 33


599

incremental nature of the insight-based approach make it more informative for the decision

600

analyst to use, by allowing them to keep control of the decision process and to gain a proper

601

understanding of the implications of various design options and their interactions, particularly

602

those across different subsystems. Therefore, it offers an effective approach for practitioners

603

to discover the superior designs of an integrated local production system for supplying food,

604

energy and water to achieve significant resource savings than relying on centralized supply,

605

through rational utilization of local renewables and resource sharing and exchange between

606

different local production processes. Whichever approach is followed, significant savings

607

have been demonstrated for a well-designed integrated local production system. However

608

there will often be practical challenges in terms of data availability, an issue to be resolved by

609

the combination of literature data sources (particularly for cumulative exergy consumption)

610

and locale-specific data gathering.

611

ASSOCIATED CONTENT

612

Supporting Information: Data and parameters used in the models and ancillary information.

613

AUTHOR INFORMATION

614

Corresponding Author

615

* Dr. Aidong Yang, Department of Engineering Science, University of Oxford, Parks Road,

616

Oxford, OX1 3PJ, United Kingdom. Email:[email protected]. Phone No.: +44

617

(0)1865 2 73094

618

ACKNOWLEDGMENT



Page 30 of 33

619

Financial support from the Leverhulme Trust through the Project Grant RPG-2012-663 is

620

greatly acknowledged. We would like to also thank three anonymous reviewers for their

621

detailed and constructive suggestions.

622

REFERENCES

623 624

1. Ecosystems and Human Wellbeing: Synthesis; Millennium Ecosystem Assessment; Island Press: Washington, DC, 2005.

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2. Curtis, F. Eco-localism and sustainability. Ecological Economics 2003, 46, 83–102.

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3. Johansson, A.; P. Kisch; M. Mirata. Distributed economies – A new engine for innovation. Journal of Cleaner Production 2005, 13 (10–11), 971–979.

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4. Martinez-Hernandez, E.; Leung Pah Hang, M. Y.; Leach, M.; Yang, A. A framework for modeling local production systems with techno-ecological interactions. Journal of Industrial Ecology 2016, in press.

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5. Leung Pah Hang, M.Y.; Martinez-Hernandez, E.; Leach, M.; Yang, A. Designing integrated local production systems: A study on the food-energy-water nexus, Journal of Cleaner Production 2016a, 135, 1065-1084.

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6. Food and Agriculture Organization of the United Nations (FAO). The Water-EnergyFood Nexus: A new approach in support of food security and sustainable agriculture. 2014, Rome.

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7. Bizikova, L.; Roy, D.; Swanson, D.; Venema, H.D.; McCandless, M. The Water– Energy–Food Security Nexus: Towards a Practical Planning and Decision-Support Framework for Landscape Investment and Risk Management, 2013, International Institute for Sustainable Development.

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9. Machell, J.; Prior, K.; Allan, R.; Andresend, J.M. The water energy food nexuschallenges and emerging solutions. Environ. Sci.: Water Res. Technol. 2015, 1, 15-16.

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10. Daher, B.T.; Mohtar, R.H. Water–energy–food (WEF) Nexus Tool 2.0: guiding integrative resource planning and decision-making. Water International 2015, 40 (56), 748-771. 11. Howells, M., et al. Integrated analysis of climate change, land-use, energy and water strategies. Nature Climate Change 2013, 3 (7), 621-626.

Hoff, H. Understanding the nexus. Background paper for the Bonn conference on the water-food-energy nexus, 2011, SEI Stockholm.


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12. Keairns, D.; Darton, R.; Irabien, A. The energy-water-food nexus. Annual review of chemical and biomolecular engineering 2016, 239-262.

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13. Singh, A. Optimal allocation of resources for the maximization of net agricultural return. Journal of Irrigation and Drainage Engineering 2012, 138 (9), 830-836.

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14. Khan, Z.; Linares, P.; García-González, J. Integrating water and energy models for policy driven applications. A review of contemporary work and recommendations for future developments. Renewable and Sustainable Energy Reviews 2017, 67, 11231138.

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15. Garcia, D.J.; You, F. The water-energy-food nexus and process systems engineering: A new focus. Computers & Chemical Engineering 2016, 91, 49-67. 16. Foo, D. Process integration for resource conservation, 2013, Taylor & Francis Group. 17. Klemes, J.J.; Varbanov, P.S.; Kravanja, Z. Recent developments in Process Integration. Chem. Eng. Res. Des. 2013, 91, 2037-2053. 18. Linnhoff, B. Pinch Analysis - A state of the art overview. Trans. IChemE 1993, 71 (A), 503-522. 19. Wang, Y.P.; Smith, R. Wastewater minimization. Chem. Eng. Sci. 1994, 49, 9811006. 20. Nelson, A.M.; Liu, Y.A. Hydrogen pinch analysis made easy. Chem Eng J 2008, 115 (6), 56–61. 21. Perry S.; Klemeš J.; Bulatov I. Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors. Energy 2008, 33 (10), 14891497. 22. Kostevsek, A.; Petek, J.; Cucek, L.; Klemes, J.J.; Varbanov, P.S. Locally Integrated Energy Sectors supported by renewable network management within municipalities. Appl. Therm. Eng. 2005, 89, 1014-1022.

23. Szargut, J.; Morris, D.; Steward, F. Exergy analysis of thermal, chemical, and metallurgical processes, 1988, Hemisphere Publishing Corporation, New York. 24. Ukidwe, N.U.; Bakshi, B.R. Thermodynamic accounting of ecosystem contribution to economic sectors with application to 1992 U.S. economy. Environ. Sci. Technol. 2004, 38 (18), 4810-4827.



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25. Dewulf, J.; Bösch, M. E.; De Meester, B.; Van der Vorst, G.; Van Langenhove, H.; Hellweg S.; Huijbregts, M. A. J. Cumulative Exergy Extraction from the Natural Environment (CEENE): a comprehensive Life Cycle Impact Assessment method for resource accounting, Environ. Sci. Technol. 2007, 41 (24), 8477–8483. 26. Dai, J.; Chen, B.; Sciubba, E. Ecological Accounting Based on Extended Exergy: A Sustainability Perspective. Environ. Sci. Technol. 2014, 48 (16), 9826–9833. 27. Leung Pah Hang, M.Y.; Martinez-Hernandez, E.; Leach, M.; Yang, A. Towards a coherent multi-level framework for resource accounting, Journal of Cleaner Production 2016b, 125 (20), 204-215. 28. Douglas, J.M. Conceptual Design of Chemical Processes, 1998, McGraw Hill, New York. 29. Sirkin, T.; Houten, M. T. The Cascade Chain–A theory and tool for achieving resource sustainability with Applications for Product Design. Resources, Conservation and Recycling 1994, 10, 213-277. 30. Whitehill and Bordon. Eco-town Master Plan [Online], 2012, Available from: http://www.whitehillbordonregeneration.co.uk/master-plan [Accessed 25 May 2017] 31. DEFRA, Family food datasets [Online], 2014, Available from: www.gov.uk/government/statistical-data-sets/family-food-datasets, [Accessed 2 November 2014] 32. DECC, Energy consumption in the UK [Online], 2015, Available from: www.gov.uk/government/uploads/system/uploads/attachment_data/file/449134/ECU K_Chapter_3_-_Domestic_factsheet.pdf, [Accessed 3 March 2015] 33. Leung Pah Hang, M. Y. Chapter 7: Robustness analysis and robust design of LIPS under uncertainties. In: Engineering design of localized synergistic production systems, PhD thesis, 2017, University of Surrey. 34. Cowell, S.; Parkinson, S. Localization of UK food production: an analysis using land area and energy as indicators. Agriculture, Ecosystems and Environment 2003, 94, 221-236. 35. UN Water, Safe use of wastewater in agriculture [Online], 2013, Available from: http://collections.unu.edu/eserv/UNU:2661/proceedings-no-11_WEB.pdf, [Accessed 31 August 2016] 36. Linnhoff, B.; Hindmarsh, E. The pinch design method for heat exchanger networks. Chem. Eng. Sci. 1983, 38, 745–763. 32 ACS Paragon Plus Environment

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