Systematic Methods for the Design of Industrial Clusters with Capped Carbon Emissions

Hydrocarbon resource centric economies, such as Qatar, are highly vulnerable to the impact of climate policy. Climate policies could decrease demand of hydrocarbon, lowering prices and would force countries to adopt mitigation technologies. Thus, having a climate strategy is important to meet future constraints. This work develops approaches to enable policy makers to systematically explore alternative emissions reduction paths in an integrated framework. The methods introduced explore the element of time, resources management, Carbon Capture Utilization and Sequestration (CCUS) and energy integration including Renewable Energy (RE) use. The industrial city or cluster is taken as a system and modelled through balances and constraints, which were optimized applying deterministic solvers. Two approaches were developed. The first is a multi-period carbon planning approach that enables the assessment of different carbon dioxide reduction options, which may be applied to guiding transitions to a future target emission. Second is a systematic approach that enables the identification of economically optimal natural gas allocation in different conversion technologies under carbon emission targets with energy synergy. The multi-period planning approach identified allocation of carbon dioxide between sources and potential sinks in each period, compared cost elements simultaneously and resulted in a low cost network across all periods. Furthermore, the role of RE was investigated through a robust MILP. The results highlighted significant differences in economic impact of alternative footprint reduction policies. The systematic natural gas monetization approach simultaneously determined natural gas monetization and carbon dioxide management through CCUS as well as RE strategies. The method considered heat and power integration, enabling the assessment of the Natural gas (CH₄), CO₂ and Energy nexus. Several case studies were solved that indicated benefits of having optimized policies that screen all mitigation options given economic and environmental objectives out preformed adopted prescribed policies found around the globe

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Application of Parametric Optimization and Control in The Smart Manufacturing of Hydrogen Systems

The main objective of this dissertation is to develop and deploy and test explicit model predictive control feedback strategy on hydrogen systems using the PARametric Optimization and Control framework (PAROC). In line with the Smart Manufacturing initiative, our endeavor explores a new model based embedded control architecture that can enable the flexibility and adaptability of hydrogen process system to artificial intelligent algorithms. First a hydrogen supply chain model is developed to identify sustainable hydrogen technologies and then explicit model predictive control is developed using the PAROC framework. Both in silico and laboratory implementations are considered towards a smart prototype system application and demonstration. In silico PAROC considerations include the development and validation of high-fidelity models based on which the application of the multi-parametric programming techniques results in the derivation of explicit optimal feedback design strategy through the solution of a receding horizon optimization problem formulation. The derived explicit parametric control strategy is validated first in silico and then in real-time. Thus, laboratory scale experimental prototypes have been designed and built. The prototypes include: (i) a metal hydride hydrogen storage system (MHSS) and (ii) a PEM Water Electrolysis (PEMWE). The MHSS is designed to replicate the refueling process of a Fuel Cell Electric Vehicle (FCEV) in a hydrogen gas station while the PEMWE is designed as a module in a large scale modular hydrogen production process. Integration of the explicit MPC feedback control strategy and the online implementation on the prototype systems create smart hydrogen energy technologies. Both prototypes are tested using the explicit model predictive control strategies developed and the results obtained from the real-time implementation of the explicit feedback strategy demonstrates the potential of the proposed strategy and effective control design that meets the desired control objectives

Modelling framework for the design of hydrogen-CCS networks to decarbonise heating and industrial clusters

This dissertation elucidates the value of H₂ and CO₂ infrastructure in decarbonising “difficult-to-abate” sectors of the economy. We analyse infrastructure for low-carbon fuels and energy vectors at different scales, and present a flexible formulation to integrate relevant technologies for their interconversion. We use a mixed integer linear programming (MILP) approach to formulate spatial systems optimisation problems, and identify solutions that can accelerate the transition to low-carbon systems. The modelling framework can incorporate spatial and temporal granularity, whilst also capturing the nuances of a site, country, or an industrial cluster. Through its application, we outline a transition pathway for the natural-gas based heating sector to H₂ in Great Britain, noting the key barriers to cost-effective deployment. The cost-optimal supply mix contains natural gas reforming with CCS, flexible electrolytic H₂ production, large volumes of salt cavern storage, and biomass gasification with CCS to offset any remaining methane and CO₂ emissions from the natural gas supply chain, and the production plants. Given the uncertainties involved, we note that a complete conversion of the gas grid in the UK to H₂ for heating buildings is unlikely to be viable. We find that a portfolio-based approach containing post-combustion CO₂ capture, fuel switching with H₂, and negative emissions is a cost-effective strategy to decarbonise industrial clusters in the UK. This achieves greater decarbonisation and avoids an overreliance on CO₂ emission offsets. The total costs of CO₂ avoidance can be reduced by using existing fuels such as refinery fuel gases for H₂ production. Natural gas plays an important role as fuel and feedstock for post-combustion and methane reforming, and is the primary determinant of the total costs of the system. This has implications for the security of supply, given that countries such as the UK import as much natural gas as they produce domestically. A cradle-to-gate lifecycle assessment of reforming, and electrolytic H₂ production using grid power and offshore wind power, shows that the lowest global warming potential is generated using a dedicated renewable-led supply. However, none of the production pathways are dominant across all key environmental performance indicators. This indicates the potential for “problem shifting” to occur by solely focussing on a given pathway for long-term supply development. We note that the environmental performance of H₂ improves with reductions in upstream methane emissions, and an increase in the capacity factor of renewable power generation assets.Open Acces

Exploration of a Digital Twin Concept for Income Maximisation of the Waikaremoana Power Scheme

Digital twins are digital emulations of real-world objects or systems which mirrors the asset in terms of both behaviour and likeness. Genesis Energy Ltd, an electricity generation company in New Zealand, is interested in developing a digital twin for one of its hydropower assets, the Waikaremoana Power Scheme (WPS). The scheme is a multi-lake cascading system in the North Island with a generation capacity of 138 MW using seven turbines. The ultimate goal for digital twin development with Genesis is to create a tool capable of providing decision making support for traders managing the utilisation of the WPS in the NZ electricity market to maximise income and efficiency while minimising losses. This project is a tentative exploration into how an early digital twin concept could be built for the WPS with the end objective of maximising utilisation through optimising unit commitment and scheduling. Plant data accuracy and reliability was examined as it is a foundational element to any digital twin. It was found that the WPS possessed accurate instruments for parameters like power output and water levels but relied on correlations for many flow readings around the scheme. Data sampling methods were also examined, and it was found that averaged data was better at short sampling intervals due to reduction of noise. A flow model was built in Microsoft Excel using a first principles-based approach, assembled using mass and energy balances along with characteristic equations for the scheme. The accuracy of the model was tested against net flow values via lake sensor readings. It was found that on average, there was a difference of around 2 m3/s for all three lakes but increased in proportion to the model net flow rate. As part of the flow model, the efficiency characteristic functions were found using a regression refinement process, starting from linear regression refining eventually to a multivariable linear (polynomial) model. The model was validated using test data from each of the generation units with excellent or good fits found for all units. . A profit-based optimisation formulation was developed based on literature and the flow model developed. The formulation was applied to a simplified case comprising of a single unit, lake, and time slice problem and testing the relationship between spot price and water value. The problem was solved using Excel Solver and a GRG nonlinear method. Depending on whether the spot price was greater or smaller than water value, the optimiser chose to generate at maximum efficiency before increasing to maximum output as spot price rose. The optimisation encountered difficulties when extending the problem to include multiple units. The Excel Solver was unable to find the global optimum without increasing compute times to unacceptable levels. To proceed further with this optimisation problem, it would need to be moved to a platform with more complex solvers

Optimal design and control of mine site energy supply systems.

The mining sector has seen an increase in costs associated with the use of energy in recent decades. Due to lower ore grade, deeper mineralization, or more remote location new mines generally require more energy to produce the same amount of mineral. Mining operations require reliable and cost-effective energy supply, without which extraction becomes economically risky, as well as unsafe for miners. Commercial software and research-oriented computer models are now available to assist in the decision making process regarding the optimal selection of Energy Supply Systems (ESS) and associated costs. However, software and models present limitations: some are designed to minimize the cost of supplying only heat and electricity, while others are custom applications for the residential and commercial sectors. Most computer tools assume invariable operating conditions, e.g. energy supply and demand profiles that do not change throughout the lifetime of the mine, or conditions whose variations can be perfectly predicted. As a result, the optimization of ESS can yield designs that lack robustness to deal with real life, changing environments. Under the same approach, the Optimal Mine Site Energy Supply (OMSES) concept was originally developed as a deterministic mathematical programming tool to find the optimal combination of energy technologies and sources that could meet final energy demands. The solution also included the optimal operation strategy based on typical energy demands of a specific mine site. This thesis expands OMSES to address the robustness of the solution, by considering the uncertainty and variability of real operating conditions. A method is proposed herein, based on the optimal solution obtained by OMSES and utilizing Model Predictive Control (MPC). The MPC-based simulation under changing environmental conditions ensures that energy demands are met at all times, taking into account energy demands and supply forecast, as well as their inherent variability. Results show that near optimal, more robust design solutions are obtained when the system is simulated under uncertain, more realistic operational conditions, leaving MPC in charge of exploring under-capacity events and of redesigning the system to ensure feasibility with minimum cost increase. This new method has been termed MPC-OMSES dynamic redesign. This thesis also reports on research work to adapt OMSES formulation to account for varying demands throughout the life of the mine, as a consequence of the natural process of mine development and extraction, which means deeper operations over time. This process entails a progressive increase in energy demands, and therefore the energy supply system must be planned accordingly. The proposed Long Term OMSES (LTOMSES) shows the advantages of considering an investment plan for the ESS, especially in the case of capital-intensive renewable energy technologies. Other concepts that have been integrated in OMSES and are covered in this thesis include: (i) material flows with considerable impact in the energy consumption have been included in the mathematical formulation, in combination with the corresponding technologies, such as pumps, fans and mobile equipment; (ii) energy and material storage have been also included, along with complex utility tariff structures, and grid and pipeline extensions. More innovative and integrated solutions can be considered by expanding the feasibility region of the optimization problem, as shown in a case study covering the integration of battery-powered electric underground mobile equipment. Overall, this thesis provides insight and tools to assist engineers in the important task of designing comprehensive and cost-effective energy supply systems for underground mines. Future work suggested includes: the development of a methodology to design fully adaptive ESS (not considering a pre-existing optimal or sub-optimal design); the simultaneous optimization of the production plan (ore extracted per day) and the design and operation of the ESS; and a dynamic approach to review the investment plan in the face of long-term environmental operating conditions.Doctor of Philosophy (PhD) in Natural Resources Engineerin