Networks
- Supply chains and transport
- Facility siting and scale
- Infrastructure and markets
MODULE 03
Deployment & Resource Allocation
What can move from a promising process to a functioning territorial system?
Connect laboratories to territories through logistics, siting, infrastructure, competition, optimization, markets and institutions. Optimal is not automatically deployable.
Open lesson →
MODULE 03Choices under scarcity
- Allocation and competing uses
- Cascading use
- LP, MILP and spatial optimization
CURRICULUM
7 lessons
222 min
01Supply chains, transport and storageIntermediate · 30 minThe physical chain between dispersed resources and steady industrial demand shapes cost, losses, emissions and reliability.02Facility siting and infrastructureIntermediate · 30 minA good site aligns feedstocks, utilities, networks, markets, permits, communities and environmental constraints at the required scale.03Resource allocation and competing usesIntermediate · 32 minFinite resources force choices among food, feed, soil, materials, fuels, energy and carbon removal—across actors and time.04Cascading use and technology competitionIntermediate · 28 minCascading seeks to preserve function and value across sequential uses, but priorities depend on evidence, markets and outcomes.05LP and MILP for bioeconomy decisionsAdvanced · 36 minLinear and mixed-integer programming translate allocations, capacities, siting and operating choices into explicit variables, objectives and constraints.06Territorial bioeconomyIntermediate · 32 minTerritories connect resource geography, industrial capability, institutions, communities and outcomes within a place-based decision system.07Why optimal is not deployableAdvanced · 34 minA model chooses within its represented world; deployment must survive omitted constraints, uncertainty, actor incentives, timing and implementation.
EVIDENCE LIBRARY
Research, models & tools
A curated starting shelf for this teaching area.
TOOLS
Related tools
LBNL / JBEI
BioSiting
A geospatial platform for exploring U.S. bioeconomy resources, infrastructure and candidate sites.
- Use it when
- What resources and infrastructure surround a candidate facility location?
- Limits
- A screening map is not a permit, supply contract or deployability decision.
Inputs, outputs & scope
- Inputs
- Location, buffer radius, resource layers, infrastructure and pathway choices.
- Outputs
- Mapped inventories, buffer summaries and downloadable spatial data.
- Typical applications
- Facility screening, resource context and early TEA/LCA framing.
Pyomo project
Pyomo
An open-source Python environment for formulating and analysing optimization models.
- Use it when
- How can decisions, objectives and constraints be represented transparently?
- Limits
- A mathematically optimal result inherits every omission and bias in the model and data.
Inputs, outputs & scope
- Inputs
- Sets, parameters, variables, objectives, constraints and a compatible solver.
- Outputs
- Feasible or optimized decisions, objective values and diagnostic information.
- Typical applications
- LP/MILP allocation, siting, supply chains, scenarios and multi-objective analysis.
U.S. Department of Energy
TECHTEST
A spreadsheet-based early-stage tool combining simplified techno-economic and life-cycle analysis.
- Use it when
- Which performance factors dominate the potential cost and energy profile of an emerging technology?
- Limits
- A screening tool does not replace a detailed process design, project finance model or critical review.
Inputs, outputs & scope
- Inputs
- Technology performance, lifetime, energy and cost assumptions, and a benchmark.
- Outputs
- Screening-level cost, energy and scenario comparisons.
- Typical applications
- Early R&D prioritization, benchmark comparison and scenario screening.
CASE STUDIES
Examples from Wang Group
2025Deployment
Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
Connects regional bamboo-residue supply, process assumptions and discounted cash flow to a deployability question for hard carbon.
2026Conversion
Ambient-pressure conversion of plastic waste to jet fuel cycloalkanes by tandem hydropyrolysis and vapour-phase hydrogenation
A plastic-to-jet-fuel conversion case for separating molecular feasibility from feedstock supply, hydrogen, certification and deployment questions.
REFERENCES
References & further reading
- Hamid Ghaderi, Mir Saman Pishvaee and Alireza Moini (2017). Biomass supply chain network design: An optimization-oriented review and analysis.https://doi.org/10.1016/j.indcrop.2016.09.027 ↗
- Welfle, Gilbert and Thornley (2014). Increasing biomass resource availability through supply chain analysis.https://doi.org/10.1016/j.biombioe.2014.08.001 ↗
- Gital Durmaz and Bilgen (2020). Multi-objective optimization of sustainable biomass supply chain network design.https://doi.org/10.1016/j.apenergy.2020.115259 ↗
- Noon and Daly (1996). GIS-based biomass resource assessment with BRAVO.https://doi.org/10.1016/0961-9534(95)00065-8 ↗
- Lawrence Berkeley National Laboratory / Joint BioEnergy Institute (2024). BioSiting Tool v2.Open source ↗
- QGIS / OSGeo (2026). QGIS Documentation.Open source ↗
- European Commission (2018). Guidance on cascading use of biomass with selected good practice examples on woody biomass.https://doi.org/10.2873/68553 ↗
- Keegan, Kretschmer, Elbersen and Panoutsou (2013). Cascading use: a systematic approach to biomass beyond the energy sector.https://doi.org/10.1002/bbb.1351 ↗
- Garcia et al. (2020). Accounting for biogenic carbon and end-of-life allocation in life cycle assessment of multi-output wood cascade systems.https://doi.org/10.1016/j.jclepro.2020.122795 ↗
- Hart, Watson and Woodruff (2011). Pyomo: modeling and solving mathematical programs in Python.https://doi.org/10.1007/s12532-011-0026-8 ↗
- Pyomo project (2026). Pyomo optimization modeling documentation.Open source ↗
- Jarosch et al. (2020). A Regional Socio-Economic Life Cycle Assessment of a Bioeconomy Value Chain.https://doi.org/10.3390/su12031259 ↗
- Eason and Cremaschi (2014). A multi-objective superstructure optimization approach to biofeedstocks-to-biofuels systems design.https://doi.org/10.1016/j.biombioe.2014.02.010 ↗
- U.S. Department of Energy (2024). Feedstock-Conversion Interface Consortium: Crosscutting Analysis Research.Open source ↗
- Senqiang Qin, Chenghao Yu, Yanghao Jin, Gaoyue Zhang, Wei Xu, Ao Wang, Mengmeng Fan, Kang Sun, Shule Wang (2025). Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon.https://doi.org/10.3390/app15137113 ↗
- Jia Wang, Zedong Zhang, Shule Wang, Ya-Fei Jiang, He Zhou, Wenhui Zhong, Yiyun Zhang, Dongxian Li, Wenjun Zhong, Shiyao Li, Daniel Sanchez, Dingsheng Wang, Jun Li, Jianchun Jiang, Yadong Li (2026). Ambient-pressure conversion of plastic waste to jet fuel cycloalkanes by tandem hydropyrolysis and vapour-phase hydrogenation.https://doi.org/10.1038/s41560-026-02078-7 ↗