624 results found
Daniel S, Kis Z, Kontoravdi K, et al., 2022, Quality by design for enabling RNA platform production processes, Trends in Biotechnology, Vol: 40, Pages: 1213-1228, ISSN: 0167-7799
RNA-based products have emerged as one of the most promising and strategic technologies for global vaccination, infectious disease control and future therapy development. The assessment of critical quality attributes, product-process interactions, relevant process analytical technologies, and process modeling capabilities can feed into a robust Quality by Design (QbD) framework for future development, design and control of manufacturing processes. Its implementation will help the RNA technology to reach its full potential and will be central in the development, pre-qualification and regulatory approval of rapid response, disease-agnostic RNA platform production processes.
Bird M, Daveau C, O'Dwyer E, et al., 2022, Real-world implementation and cost of a cloud-based MPC retrofit for HVAC control systems in commercial buildings, Energy and Buildings, Vol: 270, Pages: 1-13, ISSN: 0378-7788
Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation the building’s thermal behaviour. When simulated across two months the MPC approach performed better, able to achieve the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (1.7% compared to the baseline), while the carbon optimisation gave negligible CO2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies but impact will depend on specific building attributes.
Li K, Acha Izquierdo S, Sunny N, et al., 2022, Strategic transport fleet analysis of heavy goods vehicle technology for net-zero targets, Energy Policy, Vol: 168, ISSN: 0301-4215
This paper addresses the decarbonisation of the heavy-duty transport sector and develops a strategy towards net-zero greenhouse gas (GHG) emissions in heavy-goods vehicles (HGVs) by 2040. By conducting a literature review and a case study on the vehicle fleet of a large UK food and consumer goods retailer, the feasibilities of four alternative vehicle technologies are evaluated from environmental, economic, and technical perspectives. Socio-political factors and commercial readiness are also examined to capture non-technical criteria that influences decision-makers. Strategic analysis frameworks such as PEST-SWOT models were developed for liquefied natural gas, biomethane, electricity and hydrogen to allow a holistic comparison and identify their long-term deployment potential. Fossil and renewable natural gas are found to be effective transitional solutions. Technology innovation is needed to address range and payload limitations of electric trucks, whereas government and industry support are essential for a material deployment of hydrogen in the 2030s. Given the UK government’s plan to phase out new diesel HGVs by 2040, fleet operators should commence new vehicle trials by 2025 and replace a considerable amount of their lighter diesel trucks with zero-emission vehicles by 2030, and the remaining heavier truck fleet by 2035.
Baharudin L, Rahmat N, Othman NH, et al., 2022, Formation, control, and elimination of carbon on Ni-based catalyst during CO<inf>2</inf>and CH<inf>4</inf>conversion via dry reforming process: A review, Journal of CO2 Utilization, Vol: 61, ISSN: 2212-9820
Dry reforming of methane (DRM) promises to reduce greenhouse gas emission by converting CO2 and CH4 (produced e.g. in anaerobic digestion processes) into syngas with an almost equimolar H2/CO ratio suitable for use in Fischer-Tropsch (FT) synthesis for the production of varieties of high value chemicals and liquid fuels. Ni-based catalyst is the most viable catalyst to catalyse the reaction, but its use faces a great challenge due to its propensity to form and accumulate carbonaceous materials on its active surface. In this article, the mechanisms involved in the deactivation of Ni-based catalyst in DRM reaction by carbon deposition and other carbon-induced deactivation mechanisms, which understanding is vital for the improvement of the process, are reviewed. Based on a thorough assessment of literature, perspectives are given on ways to control and mitigate carbon deposition problems related to the use of Ni-based catalysts in DRM by means of manipulating reaction conditions and process parameters as well as through designing and developing highly active coke-resistant Ni-based catalysts.
Aunedi M, Yliruka M, Dehghan S, et al., 2022, Multi-model assessment of heat decarbonisation options in the UK using electricity and hydrogen, Renewable Energy, Vol: 194, Pages: 1261-1276, ISSN: 0960-1481
Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.
Gonzalez-Garay A, Bui M, Freire Ordóñez D, et al., 2022, Hydrogen production and its applications to mobility, Annual Review of Chemical and Biomolecular Engineering, Vol: 13, Pages: 501-528, ISSN: 1947-5438
Hydrogen has been identified as one of the key elements to bolster longer-term climate neutrality and strategic autonomy for several major countries. Multiple road maps emphasize the need to accelerate deployment across its supply chain and utilization. Being one of the major contributors to global CO2 emissions, the transportation sector finds in hydrogen an appealing alternative to reach sustainable development through either its direct use in fuel cells or further transformation to sustainable fuels. This review summarizes the latest developments in hydrogen use across the major energy-consuming transportation sectors. Rooted in a systems engineering perspective, we present an analysis of the entire hydrogen supply chain across its economic, environmental, and social dimensions. Providing an outlook on the sector, we discuss the challenges hydrogen faces in penetrating the different transportation markets.
O'Dwyer E, Falugi P, Shah N, et al., 2022, Automating the data-driven predictive control design process for building thermal management, ECOS 2022 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems
Huang Y, Kang J, Liu L, et al., 2022, A hierarchical coupled optimization approach for dynamic simulation of building thermal environment and integrated planning of energy systems with supply and demand synergy, ENERGY CONVERSION AND MANAGEMENT, Vol: 258, ISSN: 0196-8904
Leonzio G, Mwabonje O, Fennell PS, et al., 2022, Environmental performance of different sorbents used for direct air capture, Sustainable Production and Consumption, Vol: 32, Pages: 101-111, ISSN: 2352-5509
Currently, conventional carbon dioxide (CO2) mitigation solutions may be insufficient to achieve the stringent environmental targets set for the coming decades. CO2 removal (CDR) technologies, such as direct air capture (DAC), capturing CO2 from the ambient air, are required.In this research, an independent life cycle assessment (LCA) of DAC adsorption systems based on three physisorbents (metal organic frameworks) and two chemisorbents (amine functionalized sorbents) is presented. These capture processes have been optimised by us in previous work.Results show that for the overall capture process, negative CO2 emissions are ensured by using a cellulose-based amine sorbent (cradle-to-gate) ensuring even the net removal of CO2 from the atmosphere (cradle-to-grave). Processes using physisorbents have poorer performances. Chemisorbents yield operating conditions allowing lower impacts on the environment. In 2050, these processes could reduce climate change but can generate other environmental impacts.With the aim to have better environmental performances of DAC systems, future research should be focused on improving the physical properties of sorbents such as the silica gel based amine sorbent to increase their capture capacities. If metal organic frameworks are to be used, it is necessary to drop their energy consumption (by increasing the loading) and the required mass of sorbent.
Kis Z, Tak K, Ibrahim D, et al., 2022, Pandemic-response adenoviral vector and RNA vaccine manufacturing, npj Vaccines, Vol: 7, ISSN: 2059-0105
Rapid global COVID-19 pandemic response by mass vaccination is currently limited by the rate of vaccine manufacturing. This study presents a techno-economic feasibility assessment and comparison of three vaccine production platform technologies deployed during the COVID-19 pandemic: (1) adenovirus-vectored (AVV) vaccines, (2) messenger RNA (mRNA) vaccines, and (3) the newer self-amplifying RNA (saRNA) vaccines. Besides assessing the baseline performance of the production process, impact of key design and operational uncertainties on the productivity and cost performance of these vaccine platforms is evaluated using variance-based global sensitivity analysis. Cost and resource requirement projections are computed for manufacturing multi-billion vaccine doses for covering the current global demand shortage and for providing annual booster immunisations. The model-based assessment provides key insights to policymakers and vaccine manufacturers for risk analysis, asset utilisation, directions for future technology improvements and future pidemic/pandemic preparedness, given the disease-agnostic nature of these vaccine production platforms.
Bahzad H, Fennell P, Shah N, et al., 2022, Techno-economic assessment for a pumped thermal energy storage integrated with open cycle gas turbine and chemical looping technology, Energy Conversion and Management, Vol: 255, Pages: 1-23, ISSN: 0196-8904
Pumped thermal energy storage offers a high energy density, potentially resulting in a relatively low cost per unit of energy stored. In this study, two novel energy storage systems were developed. The first system was developed by integrating pumped thermal energy storage and chemical looping technologies, whereas the second was formed by merging the first system with an open cycle gas turbine. Both systems used an oxygen depleted stream as a working fluid and iron-based oxygen carriers from a chemical looping water splitting process storage material for the pumped thermal energy storage system. In addition, hydrogen from the chemical looping process was employed for the gas turbine in the second system. Both systems were evaluated thermodynamically via the determination of the roundtrip efficiency. The results presented here indicate that the roundtrip efficiency of both systems developed was 77%. Furthermore, the capital requirements, operating costs, and daily profits from electricity generation were calculated for both systems over several days within the year. The capital and operating costs for the several days that were simulated for the integrated pumped thermal energy storage system were lower than that of a gas turbine based system. Consequently, the daily profit was estimated and found to be between 4.9% and 72.9% higher for the integrated pumped storage relative to the gas turbine based system. Moreover, an economic sensitivity analysis was performed to identify the factors that strongly affect the daily profits of the gas turbine system relative to the pumped storage system. Based on the analysis, the optimal hydrogen fuel percentage fed to the open cycle gas turbine was calculated for the days simulated. Finally, the impact of % error on the estimated capital and fuel production costs on daily profits were investigated. The outcome revealed a higher impact of computational errors on the fuel costs relative to the costs of the capital.
Leonzio G, Fennell PS, Shah N, 2022, A comparative study of different sorbents in the context of direct air capture (DAC): evaluation of key performance indicators and comparisons, Applied Sciences-Basel, Vol: 12, ISSN: 2076-3417
Direct air capture can be based on an adsorption system, and the used sorbent (chemisorbents or physisorbents) influences process. In this work, two amine-functionalized sorbents, as chemisorbents, and three different metal organic frameworks, as physisorbents, are considered and compared in terms of some key performance indicators. This was carried out by developing a mathematical model describing the adsorption and desorption stages. An independent analysis was carried out in order to verify data reported in the literature. Results show that the equilibrium loading is a critical parameter for adsorption capacity, energy consumption, and cost. The considered metal organic frameworks are characterized by a lower equilibrium loading (10−4 mol/kg) compared to chemisorbents (10−1 mol/kg). For this reason, physisorbents have higher overall energy consumptions and costs, while capturing a lower amount of carbon dioxide. A reasonable agreement is found on the basis of the operating conditions of the Climeworks company, modelling the use of the same amine cellulose-based sorbent. The same order of magnitude is found for total costs (751 USD/tonneCO2 for our analysis, compared to the value of 600 USD/tonneCO2 proposed by this company)
O'Dwyer E, Indranil P, Shah N, 2022, Decarbonisation of the urban landscape: integration and optimization of energy systems, Intelligent Decarbonisation: Can Artificial Intelligence and Cyber-PhysicalSystems Help Achieve Climate MitigationTargets?, Editors: Inderwildi, Kraft, Publisher: Springer, ISBN: 978-3030862145
We highlight the key pillars of urban energy systems which would leverage on AI and digital technologiesfor a low carbon future. We summarise a couple of real world applications where optimisation, intelligentcontrol systems and cloud based infrastructure have played a transformative role in improving systemperformance, cost effectiveness and decarbonisation. The case studies show that AI and digitaltechnologies can be implemented for standalone unit operations to achieve such benefits. However, moreimportantly as the second case study shows, applying such technologies at a system level by integratingmultiple energy vectors would give much more flexibility in terms of operation, resulting in betterperformance improvements and decarbonisation strategies. We conclude by highlighting the strategictrends in this fast evolving field and giving a broad outlook in terms of cost reductions and emissionssavings for similar intelligent energy systems.
O'Dwyer E, Kerrigan EC, Falugi P, et al., 2022, Data-driven predictive control with reduced computational effort and improved performance using segmented trajectories, Publisher: ArXiv
A class of data-driven control methods has recently emerged based on Willems'fundamental lemma. Such methods can ease the modelling burden in control designbut can be sensitive to disturbances acting on the system under control. Inthis paper, we extend these methods to incorporate segmented predictiontrajectories. The proposed segmentation enables longer prediction horizons tobe used in the presence of unmeasured disturbance. Furthermore, a computationtime reduction can be achieved through segmentation by exploiting the problemstructure, with computation time scaling linearly with increasing horizonlength. The performance characteristics are illustrated in a set-point trackingcase study in which the segmented formulation enables more consistentperformance over a wide range of prediction horizons. The computation time forthe segmented formulation is approximately half that of an unsegmentedformulation for a horizon of 100 samples. The method is then applied to abuilding energy management problem, using a detailed simulation environment, inwhich we seek to minimise the discomfort and energy of a 6-room apartment. Withthe segmented formulation, a 72% reduction in discomfort and 5% financial costreduction is achieved, compared to an unsegmented formulation using aone-day-ahead prediction horizon.
Cooper N, Horend C, Roben F, et al., 2022, A framework for the design & operation of a large-scale wind-powered hydrogen electrolyzer hub, International Journal of Hydrogen Energy, Vol: 47, Pages: 8671-8686, ISSN: 0360-3199
Due to the threat of climate change, renewable feedstocks & alternative energy carriers are becoming more necessary than ever. One key vector is hydrogen, which can fulfil these roles and is a renewable resource when split from water using renewable electricity. Electrolyzers are often not designed for variable operation, such as power from sources like wind or solar. This work develops a framework to optimize the design and operation of a large-scale electrolyzer hub under variable power supply. The framework is a two-part optimization, where designs of repeated, modular units are optimized, then the entire system is optimized based on those modular units. The framework is tested using a case study of an electrolyzer hub powered by a Dutch wind farm to minimize the levelized cost of hydrogen. To understand how the optimal design changes, three power profiles are examined, including a steady power supply, a representative wind farm power supply, and the same wind farm power supply compressed in time. The work finds the compressed power profile uses PEM technology which can ramp up and down more quickly. The framework determines for this case study, pressurized alkaline electrolyzers with large stacks are the cheapest modular unit, and while a steady power profile resulted in the cheapest hydrogen, costing 4.73 €/kg, the typical wind power profile only raised the levelized cost by 2%–4.82 €/kg. This framework is useful for designing large-scale electrolysis plants and understanding the impact of specific design choices on the performance of a plant.
Li L, Wang J, Zhong X, et al., 2022, Combined multi-objective optimization and agent-based modeling for a 100% renewable island energy system considering power-to-gas technology and extreme weather conditions, APPLIED ENERGY, Vol: 308, ISSN: 0306-2619
van der Spek M, Banet C, Bauer C, et al., 2022, Perspective on the hydrogen economy as a pathway to reach net-zero CO2 emissions in Europe, Energy and Environmental Science, Vol: 15, Pages: 1034-1077, ISSN: 1754-5692
The envisioned role of hydrogen in the energy transition – or the concept of a hydrogen economy – has varied through the years. In the past hydrogen was mainly considered a clean fuel for cars and/or electricity production; but the current renewed interest stems from the versatility of hydrogen in aiding the transition to CO2 neutrality, where the capability to tackle emissions from distributed applications and complex industrial processes is of paramount importance. However, the hydrogen economy will not materialise without strong political support and robust infrastructure design. Hydrogen deployment needs to address multiple barriers at once, including technology development for hydrogen production and conversion, infrastructure co-creation, policy, market design and business model development. In light of these challenges, we have brought together a group of hydrogen researchers who study the multiple interconnected disciplines to offer a perspective on what is needed to deploy the hydrogen economy as part of the drive towards net-zero-CO2 societies. We do this by analysing (i) hydrogen end-use technologies and applications, (ii) hydrogen production methods, (iii) hydrogen transport and storage networks, (iv) legal and regulatory aspects, and (v) business models. For each of these, we provide key take home messages ranging from the current status to the outlook and needs for further research. Overall, we provide the reader with a thorough understanding of the elements in the hydrogen economy, state of play and gaps to be filled.
van de Berg D, Savage T, Petsagkourakis P, et al., 2022, Data-driven optimization for process systems engineering applications, Chemical Engineering Science, Vol: 248, ISSN: 0009-2509
Most optimization problems in engineering can be formulated as ‘expensive’ black box problems whose solutions are limited by the number of function evaluations. Frequently, engineers develop accurate models of physical systems that are differentiable and/or cheap to evaluate. These models can be solved efficiently, and the solution transferred to the real system. In the absence of gradient information or cheap-to-evaluate models, one must resort to efficient optimization routines that rely only on function evaluations. Creating a model can itself be considered part of the expensive black box optimization process. In this work, we investigate how perceived state-of-the-art derivative-free optimization (DFO) algorithms address different instances of these problems in process engineering. On the algorithms side, we benchmark both model-based and direct-search DFO algorithms. On the problems side, the comparisons are made on one mathematical optimization problem and five chemical engineering applications: model-based design of experiments, flowsheet optimization, real-time optimization, self-optimizing reactions, and controller tuning. Various challenges are considered such as constraint satisfaction, uncertainty, problem dimension and evaluation cost. This work bridges the gap between the derivative-free optimization and process systems literature by providing insight into the efficiency of data-driven optimization algorithms in the process systems domain to advance the digitalization of the chemical and process industries.
Gulliford MJS, Orlebar RH, Bird MH, et al., 2022, Developing a dynamic carbon benchmarking method for large building property estates, Energy and Buildings, Vol: 256, Pages: 111683-111683, ISSN: 0378-7788
As supermarkets are known to be energy intensive, improvements made to their efficiency can enable operators to reduce not only carbon emissions but also costs, in line with corporate and legislative targets. This study presents a novel benchmarking method to appraise emission and cost performances across a portfolio, enabling building managers to identify sites that are underperforming, taking as a case study a large number of food retail stores. Multiple layers, detailed variable selection including weather features and regression technique comparisons (Multivariate Linear Regression (MLR), Artificial Neural Network (ANN) and Decision Tree (DT)), are considered in model construction. Efficiency is evaluated on multiple bases with a focus on emissions. These are clustered together to produce a benchmark to inform investment decision-making across a portfolio. The DT technique was found to be the most effective, producing a benchmark with low average error (1.5 kgCO2 m−2 period−1) and high maximum error (21 kgCO2 m−2 period−1) indicating high accuracy and high discernment respectively. This model also correctly classified buildings known to perform poorly into the worst 30% of buildings in the portfolio. This work highlights the need for further research into natural gas consumption benchmarking and particularly the use of humidity data to better understand the issues in decarbonising heat.
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns, Computers and Chemical Engineering, Vol: 159, ISSN: 0098-1354
A key challenge in maximizing the effectiveness of model-based design of experiments for calibrating nonlinear process models is the inaccurate prediction of information that is afforded by each new experiment. We present a novel methodology to exploit prior probability distributions of model parameter estimates in a bi-objective optimization formulation, where a conditional-value-at-risk criterion is considered alongside an average information criterion. We implement a tractable numerical approach that discretizes the experimental design space and leverages the concept of continuous-effort experimental designs in a convex optimization formulation. We demonstrate effectiveness and tractability through three case studies, including the design of dynamic experiments. In one case, the Pareto frontier comprises experimental campaigns that significantly increase the information content in the worst-case scenarios. In another case, the same campaign is proven to be optimal irrespective of the risk attitude. An open-source implementation of the methodology is made available in the Python software Pydex.
Sarabia Escriva EJ, Hart M, Acha Izquierdo S, et al., 2022, Techno-economic evaluation of integrated energy systems for heat recovery applications in food retail buildings, Applied Energy, Vol: 305, ISSN: 0306-2619
Eliminating the use of natural gas for non-domestic heat supply is an imperative component of net-zero targets. Techno-economic analyses of competing options for low-carbon heat supply are essential for decision makers developing decarbonisation strategies. This paper investigates the impact various heat supply configurations can have in UK supermarkets by using heat recovery principles from refrigeration systems under different climatic conditions. The methodology builds upon a steady-state model that has been validated in previous studies. All refrigeration integrated heating and cooling (RIHC) systems employ CO2 booster refrigeration to recover heat and provide space heating alongside various technologies such as thermal storage, air-source heat pumps (ASHPs) and direct electric heaters. Seven cases evaluating various technology combinations are analysed and compared against a conventional scenario in which the building is heated with a natural gas boiler. The specific combinations of technologies analysed here contrasts trade-offs and is a first in the literature. The capital costs of these projects are considered, giving insights into their business case. Results indicate that electric heaters are not cost-competitive in supermarkets. Meanwhile, RIHC and ASHP configurations are the most attractive option, and if a thermal storage tank system with advanced controls is included, the benefits increase even further. Best solutions have a 6.3% ROI, a payback time of 16 years while reducing energy demand by 62% and CO2 emissions by 54%. Such investments will be difficult to justify unless policy steers decision makers through incentives or the business case changes by implementing internal carbon pricing.
Acha S, O’Dwyer E, Pan I, et al., 2022, Decarbonisation of the Urban Landscape: Integration and Optimization of Energy Systems, Lecture Notes in Energy, Pages: 133-144
We highlight the key pillars of urban energy systems which would leverage on AI and digital technologies for a low carbon future. We summarise a couple of real world applications where optimisation, intelligent control systems and cloud-based infrastructure have played a transformative role in improving system performance, cost-effectiveness and decarbonisation. The case studies show that AI and digital technologies can be implemented for standalone unit operations to achieve such benefits. However, more importantly as the second case study shows, applying such technologies at a system level by integrating multiple energy vectors would give much more flexibility in terms of operation, resulting in better performance improvements and decarbonisation strategies. We conclude by highlighting the strategic trends in this fast evolving field and giving a broad outlook in terms of cost reductions and emissions savings for similar intelligent energy systems.
Bernardi 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: 0098-1354
Chimeric 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.
Kusumo K, Kuriyan K, Vaidyaraman S, et al., 2022, Probabilistic framework for optimal experimental campaigns in the presence of operational constraints, Reaction Chemistry and Engineering, ISSN: 2058-9883
The predictive capability of any mathematical model is intertwined with the quality of experimentaldata collected for its calibration. Model-based design of experiments helps compute maximallyinformative campaigns for model calibration. But in early stages of model development it is crucial toaccount for model uncertainties to mitigate the risk of uninformative or infeasible experiments. Thisarticle presents a new method to design optimal experimental campaigns subject to hard constraintsunder uncertainty, alongside a tractable computational framework. This computational frameworkinvolves two stages, whereby the feasible experimental space is sampled using a probabilistic approachin the first stage, and a continuous-effort optimal experiment design is determined by searching overthe sampled feasible space in the second stage. The tractability of this methodology is demonstratedon a case study involving the exothermic esterification of priopionic anhydride with significant risk ofthermal runaway during experimentation. An implementation is made freely available based on thePython packages DEUS and Pydex.
Falugi 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: 9783030797416
Residential 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.
Ibrahim 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: 2076-393X
Vaccination 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.
For 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.
Falugi 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: 2405-8963
Buildings 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.
Kusumo KP, Morrissey J, Mitchell H, et al., 2021, A design centering methodology for probabilistic design space, 16th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Publisher: Elsevier, Pages: 79-84, ISSN: 2405-8963
The use of mathematical models for design space characterization has become commonplace in pharmaceutical quality-by-design, providing a systematic risk-based approach to assurance of quality. This paper presents a methodology to complement sampling algorithms by computing the largest box inscribed within a given probabilistic design space at a desired reliability level. Such an encoding of the samples yields an operational envelope that can be conveniently communicated to process operators as independent ranges in process parameters. The first step involves training a feed-forward multi-layer perceptron as a surrogate of the sampled probability map. This surrogate is then embedded into a design centering problem, formulated as a semi-infinite program and solved using a cutting-plane algorithm. Effectiveness and computational tractability are demonstrated on the case study of a batch reactor with two critical process parameters.
Lin 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
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