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Book chaptervan de Berg D, Petsagkourakis P, Shah N, et al., 2022,
Data-driven coordination of expensive black-boxes
, Computer Aided Chemical Engineering, Pages: 1159-1164Coordinating decision-making capacities using optimization is a key factor in the success of chemical companies. However, this coordination is often inhibited by expensive, legally-constrained, or proprietary subproblem models. We propose two variations on how model-based (surrogate) derivative-free optimization (DFO) methods can be used to coordinate subproblems with few connecting variables. When these surrogates are convex quadratic, they can be efficiently exploited using semidefinite programming techniques. We compare the performance of these two variations with a distributed optimization solver (ADMM), a model-based, and a direct DFO solver (Py-BOBYQA and DIRECTL). This comparison is done on four variations of an economic-environmental feedstock blending optimization case study. While ADMM seems to display faster initial convergence, explorative DFO optimization solvers seem promising in escaping local minimizers, especially in lower dimensions.
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Book chapterTriantafyllou N, Bernardi A, Lakelin M, et al., 2022,
A bi-level decomposition approach for CAR-T cell therapies supply chain optimisation
, Computer Aided Chemical Engineering, Pages: 2197-2202Autologous cell therapies are based on bespoke, patient-specific manufacturing lines and distribution channels. They present a novel category of therapies with unique features that impose scale out approaches. Chimeric Antigen Receptor (CAR) T cells are an example of such products, the manufacturing of which is based on the patient's own cells. This automatically: (a) creates dependencies between the patient and the supply chain schedules and (b) increases the associated costs, as manufacturing lines and distribution nodes are exclusive to the production and delivery of a single therapy. The lack of scale up opportunities and the tight return times required, dictate the design of agile and responsive distribution networks that are eco-efficient. From a modelling perspective, such networks are described by a large number of variables and equations, rendering the problem intractable. In this work, we present a bi-level decomposition algorithm as means to reduce the computational complexity of the original Mixed Integer Linear Programming (MILP) model. Optimal solutions for the structure and operation of the supply chain network are obtained for demands of up to 5000 therapies per year, in which case the original model contains 68 million constraints and 16 million discrete variables.
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Book chapterYliruka MI, Moret S, Jalil-Vega F, et al., 2022,
The Trade-Off between Spatial Resolution and Uncertainty in Energy System Modelling
, Computer Aided Chemical Engineering, Pages: 2035-2040In energy system models, computational tractability is often maintained by adopting a simplified temporal and spatial representation in a deterministic model formulation i.e., neglecting uncertainty. However, such simplifications have been shown to impact the optimal result. To address the question of how to prioritize the limited computational resources, the trade-off between spatial resolution and uncertainty is assessed by applying a novel method based on global sensitivity analysis to a peer-reviewed heat decarbonization model. For all output variables apart from the total system and fuel cost, spatial resolution is ranks amongst the five most important model inputs. It is the most relevant factor for investment decisions on network capacities. For the total fuel consumption and emissions, spatial resolution turns out to be more relevant than the fuel prices themselves. Compared across all outputs, the analysis suggests the impact of spatial resolution is comparable the impact of heat demand levels and the discount rate.
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Book chapterSoh QY, O'Dwyer E, Acha S, et al., 2022,
Model agnostic framework for analyzing rainwater harvesting system behaviors
, Computer Aided Chemical Engineering, Pages: 2023-2028To evaluate risks and characterise the responses of a rainwater harvesting system under different rainfall types, this paper presents a model agnostic evaluation framework where a k-means clustering approach is supplemented with a statistical Partial Least Squares model. Four response modes were identified for a studied system. Using these response modes, a higher risk of system overflow was found in 4.5% of simulated scenarios with inadequate water supplies found in 48.2% scenarios. The rainfall distribution in time was found to be crucial in determining the response mode of the system, with sporadic high intensity events or consistent, high total volume events allowing the system to operate in a response mode corresponding to lower system stresses, but with reduced provision of rainwater.
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Book chapterKis Z, Tak K, Ibrahim D, et al., 2022,
Quality by design and techno-economic modelling of RNA vaccine production for pandemic-response
, Computer Aided Chemical Engineering, Pages: 2167-2172Vaccine production platform technologies have played a crucial role in rapidly developing and manufacturing vaccines during the COVID-19 pandemic. The role of disease agnostic platform technologies, such as the adenovirus-vectored (AVV), messenger RNA (mRNA), and the newer self-amplifying RNA (saRNA) vaccine platforms is expected to further increase in the future. Here we present modelling tools that can be used to aid the rapid development and mass-production of vaccines produced with these platform technologies. The impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using techno-economic modelling and variance-based global sensitivity analysis. Furthermore, the use of the quality by digital design framework and techno-economic modelling for supporting the rapid development and improving the performance of these vaccine production technologies is also illustrated.
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Book chapterIbrahim D, Kis Z, Tak K, et al., 2022,
Optimal design and planning of supply chains for viral vectors and RNA vaccines
, Computer Aided Chemical Engineering, Pages: 1633-1638This work develops a multi-product MILP vaccine supply chain model that supports planning, distribution, and administration of viral vectors and RNA-based vaccines. The capability of the proposed vaccine supply chain model is illustrated using a real-world case study on vaccination against SARS-CoV-2 in the UK that concerns both viral vectors (e.g., AZD1222 developed by Oxford-AstraZeneca) and RNA-based vaccine (e.g., BNT162b2 developed by Pfizer-BioNTech). A comparison is made between the resources required and logistics costs when viral vectors and RNA vaccines are used during the SARS-CoV-2 vaccination campaign. Analysis of results shows that the logistics cost of RNA vaccines is 85% greater than that of viral vectors, and that transportation cost dominates logistics cost of RNA vaccines compared to viral vectors.
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Book chapterSarkis M, Tak K, Chachuat B, et al., 2022,
Towards Resilience in Next-Generation Vaccines and Therapeutics Supply Chains
, Computer Aided Chemical Engineering, Pages: 931-936Recent clinical outcomes of Advanced Therapy Medicinal Products (ATMPs) highlight promising opportunities in the prevention and cure of life threatening diseases. ATMP manufacturers are asked to tackle engineering product and process-related challenges, whilst scaling up production under demand uncertainty; this highlights the need for tools supporting supply chain planning under uncertainty. This study presents a computer-aided modelling and optimisation framework for viral vector supply chains. A methodology for the characterisation of process-related uncertainties is presented; the impact of input demand and process bottlenecks on optimal supply chain configurations and capacity allocations is assessed. A trade-off between cost and scalability emerges, larger costs incurring at higher input demands, whilst ensuring improved flexibility under demand uncertainty. Furthermore, bottlenecks uncertainty drives the optimisation to alternative strategic decisions, highlighting the need for a systematic integration within the framework.
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Book chapterTriantafyllou N, Bernardi A, Lakelin M, et al., 2022,
Fresh vs frozen: assessing the impact of cryopreservation in personalised medicine
, Computer Aided Chemical Engineering, Pages: 955-960Chimeric Antigen Receptor (CAR) T cell therapy is a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive haematological malignancies. Autologous CAR T cell therapies are based on bespoke manufacturing lines and distribution nodes that are exclusive to the production and delivery of a single therapy. Given their patient-specific nature, they follow a 1:1 business model that challenges volumetric scale up, leading to increased manufacturing and distribution costs. Manufacturers aim to guarantee the in-time delivery and identify ways to reduce the production cost with the ultimate objective of releasing these innovative therapies to a bigger portion of the population. In this work, we investigate upstream storage to the supply chain network as means to introduce greater flexibility in the modus operandi. We formulate and assess different supply chain networks via a Mixed Integer Linear Programming model.
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Journal articleBernardi A, Sarkis M, Triantafyllou N, et al., 2022,
Assessment of intermediate storage and distribution nodes in personalised medicine
, Computers & Chemical Engineering, Vol: 157, Pages: 107582-107582, ISSN: 0098-1354Chimeric Antigen Receptor (CAR) T cell therapies are a type of patient-specific cell immunotherapy demonstrating promising results in the treatment of aggressive blood cancer types. CAR T cells follow a 1:1 business model, translating into manufacturing lines and distribution nodes being exclusive to the production of a single therapy, hindering volumetric scale up. In this work, we address manufacturing capacity bottlenecks via a Mixed Integer Linear Programming (MILP) model. The proposed formulation focuses on the design of candidate supply chain network configurations under different demand scenarios. We investigate the effect of an intermediate storage upstream of the network to: (a) debottleneck manufacturing lines and (b) increase facility utilisation. In this setting, we assess cost-effectiveness and flexibility of the supply chain and we evaluate network performance with respect to: (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.
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Book chapterFalugi P, O’Dwyer E, Zagorowska MA, et al., 2022,
MPC and optimal design of residential buildings with seasonal storage: a case study
, Active Building Energy Systems, Editors: Doyle, Publisher: Springer International Publishing, Pages: 129-160, ISBN: 9783030797416Residential buildings account for about a quarter of the global energy use. As such, residential buildings can play a vital role in achieving net-zero carbon emissions through efficient use of energy and balance of intermittent renewable generation. This chapter presents a co-design framework for simultaneous optimisation of the design and operation of residential buildings using Model Predictive Control (MPC). The adopted optimality criterion maximises cost savings under time-varying electricity prices. By formulating the co-design problem using model predictive control, we then show a way to exploit the use of seasonal storage elements operating on a yearly timescale. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating on multiple timescales. In particular, numerical results from a low-fidelity model report approximately doubled bill savings and carbon emission reduction compared to the a priori sizing approach.
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Journal articleLin J, Zhong X, Wang J, et al., 2021,
Relative optimization potential: A novel perspective to address trade-off challenges in urban energy system planning
, APPLIED ENERGY, Vol: 304, ISSN: 0306-2619- Cite
- Citations: 16
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Journal articleRamdin M, De Mot B, Morrison ART, et al., 2021,
Electroreduction of CO2/CO to C2 products: process modeling, downstream separation, system integration, and economic analysis.
, Industrial and Engineering Chemistry Research, Vol: 60, Pages: 17862-17880, ISSN: 0888-5885Direct electrochemical reduction of CO2 to C2 products such as ethylene is more efficient in alkaline media, but it suffers from parasitic loss of reactants due to (bi)carbonate formation. A two-step process where the CO2 is first electrochemically reduced to CO and subsequently converted to desired C2 products has the potential to overcome the limitations posed by direct CO2 electroreduction. In this study, we investigated the technical and economic feasibility of the direct and indirect CO2 conversion routes to C2 products. For the indirect route, CO2 to CO conversion in a high temperature solid oxide electrolysis cell (SOEC) or a low temperature electrolyzer has been considered. The product distribution, conversion, selectivities, current densities, and cell potentials are different for both CO2 conversion routes, which affects the downstream processing and the economics. A detailed process design and techno-economic analysis of both CO2 conversion pathways are presented, which includes CO2 capture, CO2 (and CO) conversion, CO2 (and CO) recycling, and product separation. Our economic analysis shows that both conversion routes are not profitable under the base case scenario, but the economics can be improved significantly by reducing the cell voltage, the capital cost of the electrolyzers, and the electricity price. For both routes, a cell voltage of 2.5 V, a capital cost of $10,000/m2, and an electricity price of <$20/MWh will yield a positive net present value and payback times of less than 15 years. Overall, the high temperature (SOEC-based) two-step conversion process has a greater potential for scale-up than the direct electrochemical conversion route. Strategies for integrating the electrochemical CO2/CO conversion process into the existing gas and oil infrastructure are outlined. Current barriers for industrialization of CO2 electrolyzers and possible solutions are discussed as well.
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Journal articleSanchez-Vicente Y, Trusler JPM, 2021,
Saturated-phase densities of (CO2 + methylcyclohexane) at temperatures from 298 to 448 K and pressures up to the critical pressure
, Journal of Chemical and Engineering Data, Vol: 67, Pages: 54-66, ISSN: 0021-9568This work reports saturated-phase densities for the CO2 + methylcyclohexane system at temperatures between 298 and 448 K and at pressures up to the critical pressure. The densities were measured with a standard uncertainty of <1.5 kg·m–3 and were fitted along isotherms with a recently developed nonlinear empirical correlation with an absolute average deviation (ΔAAD) of about 1.5 kg·m–3. This empirical correlation also allowed the estimation of the critical pressure and density at each temperature, and the obtained critical pressures were found to be in close agreement with previously published data. We also compare both our density data and vapor–liquid equilibrium (VLE) data from the literature with the predictions from two models: PPR-78 and SAFT-γ Mie. The results show that densities were predicted better with SAFT-γ Mie than with PPR-78, whereas PPR-78 generally performed better for VLE. This could indicate that some of the unlike parameters of SAFT-γ Mie could be further optimized.
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Journal articleIbrahim D, Kis Z, Tak K, et al., 2021,
Model-based planning and delivery of mass vaccination campaigns against infectious disease: application to the COVID-19 pandemic in the UK
, Vaccines, Vol: 9, Pages: 1-19, ISSN: 2076-393XVaccination plays a key role in reducing morbidity and mortality caused by infectious diseases, including the recent COVID-19 pandemic. However, a comprehensive approach that allows the planning of vaccination campaigns and the estimation of the resources required to deliver and administer COVID-19 vaccines is lacking. This work implements a new framework that supports the planning and delivery of vaccination campaigns. Firstly, the framework segments and priorities target populations, then estimates vaccination timeframe and workforce requirements, and lastly predicts logistics costs and facilitates the distribution of vaccines from manufacturing plants to vaccination centres. The outcomes from this study reveal the necessary resources required and their associated costs ahead of a vaccination campaign. Analysis of results shows that by integrating demand stratification, administration, and the supply chain, the synergy amongst these activities can be exploited to allow planning and cost-effective delivery of a vaccination campaign against COVID-19 and demonstrates how to sustain high rates of vaccination in a resource-efficient fashion.
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Journal articleDhakal S, Tay WJ, Al Ghafri SZS, et al., 2021,
Thermodynamic properties of liquid toluene from speed-of-sound measurements at temperatures from 283.15 K to 473.15 K and at pressures up to 390 MPa
, International Journal of Thermophysics, Vol: 42, Pages: 1-40, ISSN: 0195-928XWe report the speeds of sound in liquid toluene (methylbenzene) measured using double-path pulse-echo apparatus independently at The University of Western Australia (UWA) and Imperial College London (ICL). The UWA data were measured at temperatures between (306 and 423) K and at pressures up to 65 MPa with standard uncertainties of between (0.02 and 0.04)%. At ICL, measurements were made at temperatures between (283.15 and 473.15) K and at pressures up to 390 MPa with standard uncertainty of 0.06%. By means of thermodynamic integration, the measured sound-speed data were combined with initial density and isobaric heat capacity values obtained from extrapolated experimental data to derive a comprehensive set of thermodynamic properties of liquid toluene over the full measurement range. Extensive uncertainty analysis was performed by studying the response of derived properties to constant and dynamic perturbations of the sound-speed surface, as well as the initial density and heat capacity values. The relative expanded uncertainties at 95% confidence of derived density, isobaric heat capacity, isobaric expansivity, isochoric heat capacity, isothermal compressibility, isentropic compressibility, thermal pressure coefficient and internal pressure were estimated to be (0.2, 2.2, 1.0, 2.6, 0.6, 0.2, 1.0 and 2.7)%, respectively. Due to their low uncertainty, these data and derived properties should be well suited for developing a new and improved fundamental Helmholtz equation of state for toluene.
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Journal articleXiao X, Trusler JPM, Yang X, et al., 2021,
Equation of state for solid benzene valid for temperatures up to 470 K and pressures up to 1800 MPa
, Journal of Physical and Chemical Reference Data, Vol: 50, Pages: 1-25, ISSN: 0047-2689The thermodynamic property data for solid phase I of benzene are reviewed and utilized to develop a new fundamental equation of state (EOS) based on Helmholtz energy, following the methodology used for solid phase I of CO2 by Trusler [J. Phys. Chem. Ref. Data 40, 043105 (2011)]. With temperature and molar volume as independent variables, the EOS is able to calculate all thermodynamic properties of solid benzene at temperatures up to 470 K and at pressures up to 1800 MPa. The model is constructed using the quasi-harmonic approximation, incorporating a Debye oscillator distribution for the vibrons, four discrete modes for the librons, and a further 30 distinct modes for the internal vibrations of the benzene molecule. An anharmonic term is used to account for inevitable deviations from the quasi-harmonic model, which are particularly important near the triple point. The new EOS is able to describe the available experimental data to a level comparable with the likely experimental uncertainties. The estimated relative standard uncertainties of the EOS are 0.2% and 1.5% for molar volume on the sublimation curve and in the compressed solid region, respectively; 8%–1% for isobaric heat capacity on the sublimation curve between 4 K and 278 K; 4% for thermal expansivity; 1% for isentropic bulk modulus; 1% for enthalpy of sublimation and melting; and 3% and 4% for the computed sublimation and melting pressures, respectively. The EOS behaves in a physically reasonable manner at temperatures approaching absolute zero and also at very high pressures.
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Journal articleHarris C, Jackson SJ, Benham GP, et al., 2021,
The impact of heterogeneity on the capillary trapping of CO2 in the Captain Sandstone
, International Journal of Greenhouse Gas Control, Vol: 112, Pages: 1-12, ISSN: 1750-5836A significant uncertainty which remains for CO2 sequestration, is the effect of natural geological heterogeneitiesand hysteresis on capillary trapping over different length scales. This paper uses laboratory data measured incores from the Goldeneye formation of the Captain D Sandstone, North Sea in 1D numerical simulations toevaluate the potential capillary trapping from natural rock heterogeneities across a range of scales, from cm to65m. The impact of different geological realisations, as well as uncertainty in petrophysical properties, on theamount of capillary heterogeneity trapping is estimated. In addition, the validity of upscaling trapping characteristics in terms of the Land trapping parameter is assessed. The numerical models show that the capillaryheterogeneity trapped CO2 saturation may vary between 0 and 14% of the total trapped saturation, dependingupon the geological realisation and petrophysical uncertainty. When upscaling the Land model from core-scaleexperimental data, using the maximum experimental Land trapping parameter could increase the expectedheterogeneity trapping by a factor of 3. Conversely, depending on the form of the imbibition capillary pressurecurve used in the numerical model, including capillary pressure hysteresis may reduce the heterogeneity trapping by up to 70%.
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Journal articleChambon CL, Verdía P, Fennell PS, et al., 2021,
Process intensification of the ionoSolv pretreatment: effects of biomass loading, particle size and scale-up from 10 mL to 1 L
, Scientific Reports, Vol: 11, Pages: 1-15, ISSN: 2045-2322The ionoSolv process is one of the most promising technologies for biomass pretreatment in a biorefinery context. In order to evaluate the transition of the ionoSolv pretreatment of biomass from bench-scale experiments to commercial scale, there is a need to get better insight in process intensification. In this work, the effects of biomass loading, particle size, pulp washing protocols and 100-fold scale up for the pretreatment of the grassy biomass Miscanthus giganteus with the IL triethylammonium hydrogen sulfate, [TEA][HSO4], are presented as a necessary step in that direction. At the bench scale, increasing biomass loading from 10 to 50 wt% reduced glucose yields from 68 to 23% due to re-precipitation of lignin onto the pulp surface. Omitting the pulp air-drying step maintained saccharification yields at 66% at 50 wt% loading due to reduced fiber hornification. 100-fold scale-up (from 10 mL to 1 L) improved the efficacy of ionoSolv pretreatment and increasing loadings from 10 to 20 wt% reduced lignin reprecipitation and led to higher glucose yields due to the improved heat and mass transfer caused by efficient slurry mixing in the reactor. Pretreatment of particle sizes of 1–3 mm was more effective than fine powders (0.18–0.85 mm) giving higher glucose yields due to reduced surface area available for lignin re-precipitation while reducing grinding energy needs. Stirred ionoSolv pretreatment showed great potential for industrialization and further process intensification after optimization of the pretreatment conditions (temperature, residence time, stirring speed), particle size and biomass loading. Pulp washing protocols need further improvement to reduce the incidence of lignin precipitation and the water requirements of lignin washing.
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Journal articleHennequin LM, Tan S-Y, Jensen E, et al., 2021,
Combining phytoremediation and biorefinery: Metal extraction from lead contaminated Miscanthus during pretreatment using the ionoSolv process
, INDUSTRIAL CROPS AND PRODUCTS, Vol: 176, ISSN: 0926-6690 -
Journal articleEschenbacher A, Fennell P, Jensen AD, 2021,
A Review of Recent Research on Catalytic Biomass Pyrolysis and Low-Pressure Hydropyrolysis
, ENERGY & FUELS, Vol: 35, Pages: 18333-18369, ISSN: 0887-0624- Cite
- Citations: 36
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Journal articleGalan-Martin A, Vazquez D, Cobo S, et al., 2021,
Delaying carbon dioxide removal in the European Union puts climate targets at risk
, NATURE COMMUNICATIONS, Vol: 12- Cite
- Citations: 56
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Journal articleHuang Z, Kurotori T, Pini R, et al., 2021,
Three-Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography
<jats:p>Quantification of heterogeneous multiscale permeability in geologicporous media is key for understanding and predicting flow and transportprocesses in the subsurface. Recent utilization of in situ imaging,specifically positron emission tomography (PET), enables the measurementof three-dimensional (3-D) time-lapse radiotracer solute transport ingeologic media. However, accurate and computationally efficientcharacterization of the permeability distribution that controls thesolute transport process remains challenging. Leveraging therelationship between local permeability variation and solute advectionrates, an encoder-decoder based convolutional neural network (CNN) isimplemented as a permeability inversion scheme using a single PET scanof a radiotracer pulse injection experiment as input. The CNN consistsof Densely Connected Neural Networks that can accurately capture the 3-Dspatial correlation between the permeability and the radiotracer solutearrival time difference maps in geologic cores. We first test theinversion accuracy using 500 synthetic test datasets. We then use asuite of experimental PET imaging datasets acquired on four differentgeologic cores. The network-inverted permeability maps from the geologiccores are used to parameterize forward numerical models that aredirectly compared with the experimental PET imaging datasets. Theresults indicate that a single trained network can generate robust,denoised 3-D permeability inversion maps in seconds. Numerical modelsparameterized with these permeability maps closely capture theexperimental solute arrival time behavior. This approach presents anunprecedented improvement for efficiently characterizing multiscalepermeability heterogeneity in complex geologic materials.</jats:p>
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Journal articleLuo H, Barrio J, Sunny N, et al., 2021,
Progress and Perspectives in Photo- and Electrochemical-Oxidation of Biomass for Sustainable Chemicals and Hydrogen Production
, ADVANCED ENERGY MATERIALS, Vol: 11, ISSN: 1614-6832- Cite
- Citations: 130
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Journal articleKuusela P, Pour-Ghaz M, Pini R, et al., 2021,
Imaging of reactive transport in fractured cement-based materials with X-ray CT
, Cement and Concrete Composites, Vol: 124, Pages: 1-12, ISSN: 0958-9465The need to improve the understanding of the properties of cement-based materials calls for the development of tools for visualizing and quantifying chemicalreactions and flows of fluids within them. In this paper, we report the results ofan experimental study where a sample of fractured cement-paste was subjectedto injection of fluids (krypton, CO2 and water) and imaged simultaneously byX-ray computed tomography (CT). Initial porosity of the sample was estimatedusing a subtraction method based on CT scans taken initially and during krypton injection. The CT reconstructions were segmented to visualize crack patterns and fluid flow in three-dimensions and to quantify the evolution of porosityduring the experiment. The results show that CT captures the formation of acarbonate phase in the sample during CO2 injection, and the flow of water inthe fractured media. We quantify the reduction of porosity resulting from thecarbonation reaction. We observe that the newly formed carbonated layer impedes water flow and, locally, can lead to crack healing. The results demonstratethe ability of CT to image reactive transport in cement-based materials, andsupport the feasibility of this imaging tool for their characterization.
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Journal articleMac Dowell N, Sunny N, Brandon N, et al., 2021,
The hydrogen economy: A pragmatic path forward
, Joule, Vol: 5, Pages: 2524-2529, ISSN: 2542-4351For hydrogen to play a meaningful role in a sustainable energy system, all elements of the value chain must scale coherently. Advocates support electrolytic (green) hydrogen or (blue) hydrogen that relies on methane reformation with carbon capture and storage; however, efforts to definitively choose how to deliver this scaling up are premature. For blue hydrogen, methane emissions must be minimized. Best in class supply chain management in combination with high rates of CO2 capture can deliver a low carbon hydrogen product. In the case of electrolytic hydrogen, the carbon intensity of power needs to be very low for this to be a viable alternative to blue hydrogen. Until the electricity grid is deeply decarbonized, there is an opportunity cost associated with using renewable energy to produce hydrogen, as opposed to integrating this with the power system. To have a realistic chance of success, net zero transition pathways need to be formulated in a way that is coherent with socio-political-economic constraints.
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Journal articleAljeshi YA, Taib MBM, Trusler JPM, 2021,
Modelling the diffusion coefficients of dilute gaseous solutes in hydrocarbon liquids
, International Journal of Thermophysics, Vol: 42, ISSN: 0195-928XIn this work, we present a model, based on rough hard-sphere theory, for the tracer diffusion coefficients of gaseous solutes in non-polar liquids. This work extends an earlier model developed specifically for carbon dioxide in hydrocarbon liquids and establishes a general correlation for gaseous solutes in non-polar liquids. The solutes considered were light hydrocarbons, carbon dioxide, nitrogen and argon, while the solvents were all hydrocarbon liquids. Application of the model requires knowledge of the temperature-dependent molar core volumes of the solute and solvent, which can be determined from pure-component viscosity data, and a temperature-independent roughness factor which can be determined from a single diffusion coefficient measurement in the system of interest. The new model was found to correlate the experimental data with an average absolute relative deviation of 2.7 %. The model also successfully represents computer-simulation data for tracer diffusion coefficients of hard-sphere mixtures and reduces to the expected form for self-diffusion when the solute and solvent become identical.
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Journal articleAnsari H, Rietmann E, Joss L, et al., 2021,
A shortcut pressure swing adsorption analogue model to estimate gas-in-place and CO2 storage potential of gas shales
, Fuel: the science and technology of fuel and energy, Vol: 301, Pages: 1-13, ISSN: 0016-2361Natural gas extraction from shale formations has experienced a rapid growth in recent years, but the low recovery observed in many field operations demonstrates that the development of this energy resource is far from being optimal. The ambiguity in procedures that account for gas adsorption in Gas-in-Place calculations represents an important element of uncertainty. Here, we present a methodology to compute gas production curves based on quantities that are directly accessed experimentally, so as to correctly account for the usable pore-space in shale. We observe that adsorption does not necessarily sustain a larger gas production compared to a non-adsorbing reservoir with the same porosity. By analysing the entire production curve, from initial to abandonment pressure, we unravel the role of the excess adsorption isotherm in driving this behaviour. To evaluate scenarios of improved recovery by means of gas injection, we develop a proxy reservoir model that exploits the concept of Pressure Swing Adsorption used in industrial gas separation operations. The model has three stages (Injection/Soak/Production) and is used to compare scenarios with cyclic injection of CO2 or N2. The results show that partial pressure and competitive adsorption enhance gas production in complementary ways, and reveal the important trade-off between CH4 recovery and CO2 storage. In this context, this proxy model represents a useful to tool to explore strategies that optimise these quantities without compromising the purity of the produced stream, as the latter may introduce a heavy economic burden on the operation.
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Journal articleFan D, Pini R, Striolo A, 2021,
A seemingly universal particle kinetic distribution in porous media
, Applied Physics Letters, Vol: 119, Pages: 1-6, ISSN: 0003-6951We study many-particle transport in randomly jammed packing of spheres at different particle Péclet numbers (𝑃𝑒∗). We demonstrate that a modified Nakagami-m function describes particle velocity probability distributions when particle deposition occurs. We assess the universality of said function through comparison against Lagrangian simulations of various particle types as well as experimental data from the literature. We construe the function's physical meaning as its ability to explain particle deposition in terms of 𝑃𝑒∗ and the competition between distributions of energy barriers for particle release and particles' diffusive energy.
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Journal articleNegri V, Galan-Martin A, Pozo C, et al., 2021,
Life cycle optimization of BECCS supply chains in the European Union
, APPLIED ENERGY, Vol: 298, ISSN: 0306-2619- Author Web Link
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- Citations: 31
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Conference paperFalugi P, O'Dwyer E, Kerrigan EC, et al., 2021,
Predictive control co-design for enhancing flexibility in residential housing with battery degradation
, 7th IFAC Conference on Nonlinear Model Predictive Control, Publisher: Elsevier, Pages: 8-13, ISSN: 2405-8963Buildings are responsible for about a quarter of global energy-related CO2 emissions. Consequently, the decarbonisation of the housing stock is essential in achieving net-zero carbon emissions. Global decarbonisation targets can be achieved through increased efficiency in using energy generated by intermittent resources. The paper presents a co-design framework for simultaneous optimal design and operation of residential buildings using Model Predictive Control (MPC). The framework is capable of explicitly taking into account operational constraints and pushing the system to its efficiency and performance limits in an integrated fashion. The optimality criterion minimises system cost considering time-varying electricity prices and battery degradation. A case study illustrates the potential of co-design in enhancing flexibility and self-sufficiency of a system operating under different conditions. Specifically, numerical results from a low-fidelity model show substantial carbon emission reduction and bill savings compared to an a-priori sizing approach.
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