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The main focus of this thesis is on the development of new techniques for the rational design of mixtures, based on a computer-aided mixture/blend design (CAMbD) framework, with applications to the chemical industry. Systematic CAMbD approaches for the design of mixtures and blends have the potential to deliver better products and processes and they enhance innovation in a highly competitive environment. In many existing mixture design methodologies, a simplified reduced version of the CAMbD problem is posed and solved, where the number of mixture ingredients is fixed in advance (usually a binary mixture is designed) and the identity of at least one compound is chosen from a given set of candidate molecules. A key achievement of this work is the development of a novel comprehensive and systematic approach for the formulation and solution of the general mixture problem where the number, identity and composition of mixture constituents are optimized simultaneously. A logic-based method, generalized disjunctive programming (GDP), is integrated for the first time into the CAMbD framework to formulate the discrete choices of mixture problems. In working towards creating a general CAMbD model, the standard (restricted) CAMbD problem is first formulated for the design of multicomponent mixtures (without focusing only on the design of binary mixtures), where the number of mixture ingredients is fixed a priori. Next, the mixture formulation is generalized by making the number, N, of components in the mixture a variable and optimising at the same time the three main decision variables of the problem, i.e., the number, identity and composition of the compounds that participate in the mixture. In the restricted and general models, the components are selected from a given list of candidate molecules. The GDP formulations are converted into mixed-integer form using the big-M (BM) approach in order to exploit the existing MINLP algorithms. The design methodology is demonstrated through a case study involving solid-liquid equilibrium calculations, where optimal solvent mixtures are determined for maximising the solubility of a drug. Solving the mixed integer optimization problems derived using BM can be challenging due to nonconvexities in the space of the continuous variables and a large combinatorial solution space which may lead to several numerical difficulties. To address the difficulties arising from the complexity of the models and facilitate problem formulation, the use of different relaxation techniques, including the big-M approach and Hull reformulations (HR), is investigated to convert the disjunctive constraints into mixed-integer form. Both solution strategies (i.e., BM and HR) are applied successfully to two case studies where optimal solvent mixtures that dissolve ibuprofen and separate acetic acid from water in a single stage liquid extraction process, respectively, are defined. The concept of a truly general approach for mixture design, where the optimal components that participate in mixtures are not selected from restricted lists or databases, is considered. In this general formulation, the molecules are designed (built) from an extensive set of atom groups, leading to the design of countless new and/or existing molecules and mixtures. The general methodology is once again applied to the design of solvent mixtures for separation processes, including crystallization and liquid extraction. First, the design of optimal solvent and antisolvent mixtures for cooling and drowning out crystallization, respectively, is resented. Next, optimal solvent mixtures are designed to separate acetic acid from water in a single-stage liquid extraction process. Integer cuts are introduced to the general mixture formulations and a list of optimal solutions (i.e., list of mixtures with different number, identity and compositions of ingredients) is obtained for each problem. The overall proposed mixture design approach paves the way for identifying innovative solutions (e.g., new molecular structures, mixtures, property functions) which play an integral role in the development of process, chemical and biochemical technologies. Part of the work presented in this thesis has been published in Jonuzaj and Adjiman [2016, 2017] and Jonuzaj et al. [2016].

This thesis is based around the production of H2 via a process called Sorbent Enhanced Steam Reforming (SESR), which has the potential to drastically reduce CO2 emissions from H2 production therefore abating climate change. SESR is a combination of traditional Steam Methane Reforming (SMR) and Calcium Looping (CaL, a form of Carbon Capture and Storage, CCS), whereby the CaL sorbent removes CO2 from the gas stream and drives the reactions equilibrium to produce more H2. With the application of biomass as the fuel feedstock this process has the potential to produce H2 with net-negative CO2 emissions. Two main areas of work were conducted within this thesis, namely: examining the performance of CaL sorbents and novel sorbents under industrial conditions, and the development of combined multifunctional sorbent and catalyst particles for SESR with biomass. These particles were tested within a reactor that was specifically constructed for this set of work. A detailed investigation into the effects of various parameters on the rate of limestone calcination was conducted within an atmospheric fluidised bed reactor utilising high-temperatures and CO2 partial pressures. These conditions would typically be present within a CaL/SESR calciner and thus kinetic data with this environment was determined. Further to this, a single particle model was developed to shed light on the variances observed when calcining limestone in the presence of CO2 and steam or CO2 and N2. The application of a novel synthetic sorbent was also investigated under similar calcination conditions showing the impacts on the sorbents carrying capacity caused by high-temperature CO2 sintering. A mild-pressure, high-temperature spout-fluidised bed reactor was constructed for the purpose of continuous biomass feeding for SESR. Combined particles of catalyst, sorbent and a dicalcium silicate support were tested. These combined particles were manufactured utilising a simple and inexpensive method. An enhanced H2 yield of 120 gH2/kgbiomass was produced at ~75 % purity at a stoichiometric steam to carbon ratio.

The increasing contributions of renewable energy sources into the electricity grid necessitates large-scale energy storage to balance supply and demand due to their inherent intermittency. Storing electrical energy in chemical bonds by electrolysis of CO2 and/or H2O is one option. The aims of this project were to develop and characterise (micro)-tubular solid oxide electrolysers for the reduction of CO2 and/or steam at temperatures of 700–800 °C. Micro-tubular hollow fibre reactors were fabricated by phase inversion. Ni(O) – yttria stabilised zirconia (YSZ) cermet electrodes (electrolysis cathode) and YSZ electrolyte (15-50 μm) were simultaneously co-extruded and sintered, followed by the application of a lanthanum strontium doped manganite (LSM) – YSZ|LSM electrode (electrolysis anode) onto the outer surface, which was subsequently sintered. At 800 °C, current densities of up to -1.0 A cm-2 were achieved at ca. 1.8 V for CO2 electrolysis with a silver wire and silver conductive paste cathodic lumen current collector. Replacing the silver wire with nickel and removing any paste additives resulted in a 50 % increase in current density. Electrode polarization for steam and co-electrolysis (H2O/CO2 co-feed) was 62-382 % lower compared to CO2 electrolysis, with the extent depending on the current collector design; the silver paste had a greater detrimental effect on the electrode performance of the SOE operating with CO2. Evidence supporting dual-step co-electrolysis with electro-generation of hydrogen preceding the heterogeneous chemical reaction of H2 with CO2 included electrochemical performance, adsorption modelling, diffusion considerations, and response to silver paste. However, isotopic studies to differentiate between (electro)chemical processes using labelled C18O2 and H216O were inconclusive due to oxygen-18 exchange occurring between C18O2 and H216O, within the alumina feed tube, despite the absence of a Ni-YSZ cathode acting as a catalyst. To further characterize the intrinsic CO2 reduction mechanism, the surface exchange kinetics of C18O2 on YSZ and oxide diffusion coefficients, without electrochemical polarization, were determined using secondary ion mass spectrometry. These results facilitated the analyses of SOE experiments using oxygen-18 tracers that compared the effect of applied current on oxide ion transport rates within the hollow fibre reactors. Techno-economical evaluation of intra-day energy storage using the micro-tubular reactors cyclically in electrolyser and fuel cell operational mode resulted in an electricity storage cost of £0.016 per kWh, considering capital and operating costs (assuming £0.1 per kWh electricity costs), which is lower than current pumped hydroelectric storage (£0.05 per kWh).

Influenced by environmental concerns and rapid urbanisation, cities are changing the way they historically have produced, distributed and consumed energy. In the next decades, cities will have to increasingly adapt their energy infrastructure if new low carbon and smart technologies are to be effectively integrated. In this context, advanced planning tools can become crucial to successfully design these future urban energy systems. However, it is not only important to analyse how urban energy infrastructure will look like in the future, but also how they will be operated. Advanced energy management strategies can increase the operational efficiency, therefore reducing energy consumption, CO2 emissions, operational costs and network investments. However, the design and analysis of these energy management strategies are difficult to perform at an urban scale considering the spatial and temporal resolution and the diversity in users energy requirements. This thesis proposes a novel integrated modelling framework to analyse flexible transport and heating energy demand and assess different demand-side management strategies in urban energy systems. With a combination of agent-based simulation and multi-objective optimisation models, this framework is tested using two case studies. The first one focuses on transport electrification and the integration of electric vehicles through smart charging strategies in an urban area in London, UK. The results of this analysis show that final consumer costs and carbon emissions reductions (compared to a base case) are in the range of 4.3-45.0% and 2.8-3.9% respectively in a daily basis, depending on the type of tariff and electricity generation mix considered. These reductions consider a control strategy where the peak demand is constrained so the capacity of the system is not affected. In the second case study, focused on heat electrification, the coordination of a group of heat pumps is analysed, using different scheduling strategies. In this case, final consumer costs and carbon emissions can be reduced in the range of 4-41% and 0.02-0.7% respectively on a daily basis. In this case, peak demand can be reduced in the range of 51-62% with respect to the baseline. These case studies highlight the importance of the spatial and temporal characterisation of the energy demand, and the level of flexibility users can provide to the system when considering a heterogeneous set of users with different technologies, energy requirements and behaviours. In both studies, trade-offs between the environmental and economic performance of demand-side management strategies are assessed using a multi-objective optimisation approach. Finally, further applications of the integrated modelling framework are described to highlight its potential as a decision-making support tool in sustainable and smart urban energy systems.

The Climate Change Act constitutes legally binding legislation that requires the UK to reduce its greenhouse gas emissions by 80 per cent by 2050 relative to 1990. Decarbonising the electricity and heat sectors is fundamental to achieving this target. Since the challenge could potentially involve several million heating installations and several thousand megawatts of generation, there is a pressing need for tools that can provide insights into the impacts stemming from this transition. Previous studies make use of coarse simplifications of links between the heat and electricity sectors. A primary contribution of this work is the linking of detailed models of electricity supply and residential heating. Analysis of future electricity price formation leads to questions regarding the financing of dispatch-able generation. Furthermore, these issues are seen to influence performance of residential heating systems. In a low carbon future, fuel cell based micro cogeneration shows the most economic potential but is the worst performer in terms of emissions, and economic value can be eroded by low ramping limits and high minimum set points. Stirling engine based micro cogeneration is not economic in any of the scenarios considered due to low utilisation. Although heat pumps produce the lowest emissions, investment does not yield economic gains unless they are incentivised to consume inexpensive excess low carbon generation. Resistive heaters are likely to be chosen over heat pumps in this setting since they are cheaper to install and produce significant economic benefits for consumers, though this could lead to significantly higher primary energy consumption with related environmental impacts. Overall, this work has demonstrated that an important dynamic exists between electricity sector decarbonisation, market arrangements that drive electricity prices and technology choices in residential heating systems. Policy makers should be mindful of this dynamic when designing markets and policy instruments of the future.

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