26 European Symposium on Computer Aided Process Engineering

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European Federation of Chemical Engineering - Electrochemical Engineering

Sorry, this product is currently unavailable. Sorry, this product is currently out of stock. Flexible - Read on multiple operating systems and devices. Energy, water and waste systems analyzed at a nexus level is key to move towards more sustainable cities.

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Three categories of waste including wastewater WW , municipal solid waste MSW and agriculture waste are tested as the feedstock for thermochemical treatment via incineration, gasification or pyrolysis for combined heat and power generation, or biological treatment such as anaerobic digestion AD and aerobic treatment. A case study is presented for Ghana in Sub-Saharan Africa, considering a combination of waste treatment technologies and infrastructure, depending on local characteristics for supply and demand. The results indicate that the biogas generated from waste treatment turns out to be a promising renewable energy source in the analyzed region, while more distributed energy resources can be integrated.

  1. Radiographic Anatomy.
  2. Requirements.
  3. 25th European Symposium on Computer Aided Process Engineering - DTU Orbit.

A series of scenarios including the business-as-usual, base case, natural constrained, policy interventions and environmental and climate change impacts demonstrate how simulation with optimization models can provide new insights in the design of sustainable value chains, with particular emphasis on whole-system analysis and integration. It is crucial for sustainable planning to consider broad environmental and social dimensions and systemic implications of new infrastructure to build more resilient societies, reduce poverty, improve human well-being, mitigate climate change and address other global change processes.

This article presents resilience.

  2. ESCAPE (27th European Symposium on Computer-Aided Process Engineering).
  3. Forsaking All Others;
  4. ESSAYS IN MEDICAL ETHICS: plea for a medicine of prudence.
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We then use Mixed-Integer Linear Programming to optimise a multi-objective function to find cost-optimal solutions, inclusive of environmental metrics such as greenhouse gas emissions. The solutions in space and time provide planning guidance for conventional and novel technology selection, changes in network topology, system costs, and can incorporate any material, waste, energy, labour or emissions flow.

Developing countries struggle to implement suitable electric power and water services, failing to match infrastructure with urban expansion.

Integrated modelling of urban water and power systems would facilitate the investment and planning processes, but there is a crucial gap to be filled with regards to extending models to incorporate the food supply in developing contexts. In this paper, a holistic methodology and platform to support the resilient and sustainable planning at city region level for multiple sectors was developed for applications in urban energy systems UES and the energy-water-food nexus, combining agent-based modelling - to simulate and forecast resource demands on spatial and temporal scales - with resource network optimization, which incorporates capital expenditures, operational costs, environmental impacts and the opportunity cost of food production foregone OPF.

Via a scenario based approach, innovative water supply and energy deployment policies are presented, which address the provision of clean energy for every citizen and demonstrate the potential effects of climate change. The results highlighted the vulnerability of Ghanas power generation infrastructure and the need for diversification. Feed-in tariffs and investment into supporting infrastructure and agriculture intensification will effectively increase the share of renewable energy and reduce carbon emissions.

Stationary batteries could facilitate provision of carbon arbitrage services in cities. Such services offer a smart solution to integrate low-carbon energy technology into grid electricity supply and help tackle climate change. In this paper the environmental implications and overall profitability of this approach are assessed. A modelling framework has been developed to design an energy storage system with optimal capacity to maximise carbon savings. The City of London was used as a case study to demonstrate model applicability and analyse the potential effect of intermittent renewable energy sources in the supply system.

The total savings obtained for the carbon arbitrage service were economically valorised using carbon market prices. In addition, a critical profitability thresholds for carbon trading prices are identified. Results show that this approach could bring environmental benefits depending on the carbon intensity of the grid, but that high carbon trading prices are required before it is economically feasible. To tackle prominent societal challenges such as increasing energy demands and climate change due to greenhouse gas GHGs emissions demand side management DSM and distributed generation DG have been proposed as effective solutions particularly for urban areas with high energy densities and diverse types of energy demands.

However, urban energy systems are complex, involving supply-demand interconnections, interaction of whole system level with various stakeholders e. The potential roles of integrated DSM and DG for the climate change mitigation under the urban energy system context haven been yet well understood.

Our research aims to advance the understanding of such promising urban energy solutions and generate new insights via modelling framework development. In this study, an optimisation model is developed to simulate the combined effects of DSM and DG strategies in the optimal design of urban energy systems, and investigate the trade-offs between environmental and economic targets.

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The results of our case study on a hypothetical urban area suggest that the effects of just DSM on climate change mitigation are relatively low whereas urban system would benefit significantly from the introduction of more carbon efficient and economically competitive DG technologies. This study demonstrates the insights such model could provide for the decision-making and paradigm shifts towards sustainable urban energy systems and smart operational strategies. Current urban water and energy systems are expanding while increasing attention is paid to their social, economic and environmental impacts.

As a research contribution that can support real-world decision making and transitions to sustainable cities and communities, we have built a model-based and data-driven platform combining comprehensive database, agent-based simulation and resource technology network optimization for system level water and energy planning.

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The outputs depict an overall resource landscape of the studied urban area, but also provide the energy, water, and other resource balance of supply and demand from both macro and micro perspectives, which is used to propose environmental friendly and cost effective sustainable city development strategies. This work is to become a core component of the resilience. Modelling socio-technical systems in which a population of heterogeneous agents generates demand for infrastructure services requires a synthetic population of agents consistent with aggregate characteristics and distributions.

A synthetic population can be created by generating individual agents with properties and rules based on a scenario definition. Simulation results fine-tune this process by comparing system level behaviour with external data, after which the emergent behaviour can be used for analysis and optimisation of planning and operation. An example of electricity demand profiles is used to illustrate the approach. To reduce this environmental impact it is important to design efficient energy infrastructures able to deal with high level of renewable energy resources.

A crucial element in this design is the quantitative understanding of the dynamics behind energy demands such as transport, electricity and heat. In this paper an agent-based simulation model is developed to generate residential energy demand profiles in urban areas, influenced by factors such as land use, energy infrastructure and user behaviour. Within this framework, impact assessment of low carbon technologies such as plug-in electric vehicles and heat pumps is performed using London as a case study.

The results show that the model can generate important insights as a decision support tool for the design and planning of sustainable urban energy systems. If electricity infrastructures are to make the most of electric vehicle EV technology it is paramount to understand how mobility can enhance the management of assets and the delivery of energy. This research builds on a proof of concept model that focuses on simulating EV movements in urban environments which serve to forecast EV loads in the networks.

Having performed this analysis for a test urban environment, this paper details a case study for London using an activity-based model to make predictions of EV movements which can be validated against measured transport data. Results illustrate how optimal EV charging can impact the load profiles of two areas in central London - St.