(733d) Resilient and Sustainable Bioenergy Systems Modelling

Society faces a number of challenges over the coming decades including climate change and increasing resource demands associated with an expanding global population. A common factor that unifies such challenges is how demand for energy, which is projected to increase by up to 40% by 2035 ^[1], can be met in a way that balances environmental, economic and societal goals. Targets emerging from COP21 to explore routes that could limit global climate change to 1.5°C, suggest that deployment of bioenergy (from biomass or waste resource) coupled with other renewable energy technologies is likely to play a role in meeting future energy demands.

However, the isolated sub-systems, system complexity and requirement for cross-disciplinary research hinder the paradigm shift to sustainable yet resilient bioenergy clusters. Under bioenergy systems, carbon and nitrogen cycles are involved, where carbon and nitrogen act as life building blocks, circulate between living organisms and environments via biochemical cycles. Such cycles enable bioenergy systems to be circular by nature. Fundamentally, the interaction between living organisms and ultimately agro-ecosystem functions and the biomass production are underpinned by photosynthesis. Out of three photosynthesis pathways, Calvin-Benson-Bassham cycle (C3 plant e.g. wheat, willow, poplar) and Hatch-Slack cycle (C4 plants e.g. maize, sugarcane, miscanthus) ^[2] have been the research focus as lignocellulosic resources for bioenergy generation. Their photosynthetic reactions and biochemical regulation differ substantially. Not only regulated by plant internal physiological traits and metabolism pathways, their photosynthetic efficiencies are also influenced by a range of external environmental drivers (e.g. atmospheric CO₂ levels^[3] temperature^[2]) at spatial scales. Thereby, plant biomass production and chemical composition vary significantly with plant species and also show spatial-temporal variation and uncertainties. In addition, bioenergy sectors are interconnected with natural capital (land, water, air) and built environment (e.g. energy transmission) and constrained by their capacities across spatial and temporal scales; in particular, bioenergy compete with other bio-products on the same natural capital resources. Moreover interactive stakeholder groups (so called agents) are involved in the bioenergy systems, which can be generalised as five supply chain echelons (resource supply, manufacturing, warehouse, distribution, market). A range of challenges remain unsolved - natural capital resource-competition (bioenergy vs. non-energy)^[4], trade-offs between sustainability criteria, multi-echelon interaction and decision under temporal, spatial and environmental variability.

Overall, bioenergy system complexity spans from the plant-soil-climate interaction underpinned by photosynthesis and biogeochemical cycles to the interconnected sub-systems; research covers several subjects across life sciences, environmental sciences, chemical engineering and process s engineering. Such system complexity and high inter-disciplinarity could be tackled using overarching models, which bridge several fundamental and applied perspectives at the boarder of Natural Sciences and Chemical Engineering. Despite the modelling advances in photosynthesis, biogeochemical simulation, manufacturing process simulation, sustainability evaluation and system optimisation in the fields of bioenergy, an overarching modelling approach remains unexplored.

The overarching framework presented under this study integrates the process-based biogeochemical models ^{[5, 6]} with manufacturing process simulation-evaluation for bioenergy which represents a research frontier at science-engineering interface. This links C3 and C4 plant growth underpinned by differed photosynthetic reactions and biochemical regulation with bioenergy manufacturing; this approach brings biogeochemical cycles and soil-plant-climate interaction into the land use decision framework, which underlines the terrestrial biomass-dependent bioenergy design and enable multi-echelon resource optimisation to account for wider environmental variables and ecosystems. Mixed integer programming (MIP) has been applied to develop the spatial-temporal bioenergy optimisation model which has been formulated to optimise the bioenergy system under both centralised and competitive environments and accounted for multi agents with conflicting objectives, biomass and bio-energy vectors and energy deployment policy design variables. The developed MIP model has been further linked with the biogeochemical model and bioenergy design tools with simulation-evaluation loops, which inherently links the empirical advances and biochemical and thermochemical technology sub-systems and captures the system-wide economic and environmental performances. Such modelling framework has been applied to bioenergy use cases in countries under the context of both developed economy and Global South, which reflect nation-specific environmental and socio-economic threats and driving forces for bioenergy penetration e.g. energy accessibility and security, renewable job availability. The case studies generate knowledge on the research merit of C3 and C4 plant biomass and bioenergy conversion technologies for a resource-efficient system under future climate scenarios and optimal configuration for the representative energy systems at national level to support sustainable natural capital resource use. Moreover, our research also helps to identify the optimised bioenergy solutions that deliver sustainable agriculture intensification in terms of natural capital (land/water) uses and are resilient to future environmental and demographical changes. The overarching model development and applications presented in this study highlight the research advances the trans-disciplinary approach could bring to achieve a step-change towards a sustainable and resilience energy future.

Acknowledgement

Author wish to acknowledge UK EPSRC for providing financial support for the Fellowship project ‘Resilient and Sustainable Biorenewable Systems Engineering Model (ReSBio)’ and all the partners for their continuous support and ongoing collaboration under ReSBio.

1. IEA, Resources to Reserves 2013. 2013, International Energy Agency.
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