Imperial College London

ProfessorNilayShah

Faculty of EngineeringDepartment of Chemical Engineering

Professor of Process Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6621n.shah

 
 
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Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 5557

 
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Location

 

ACEX 304/5ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

603 results found

O'Dwyer E, Indranil P, Shah N, 2022, Intelligent Decarbonisation: Can Artificial Intelligence and Cyber-Physical Systems Help Achieve Climate Mitigation Targets?, Publisher: Springer, ISBN: 978-3030862145

Book

Li L, Wang J, Zhong X, Lin J, Wu N, Zhang Z, Meng C, Wang X, Shah N, Brandon N, Xie S, Zhao Yet 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

Islands are constrained by geographical conditions in terms of energy delivery. Due to weak connections with the mainland and the power grid, the diversity of island energy demand leads to high economic costs and environmental pollution issues. This study proposes a 100% renewable island energy system, which integrates with power-to-gas, combined cooling, heating and power, and desalination technologies to supply electricity, heating, cooling, gas and fresh water to the local residents. A comprehensive approach for energy demand prediction, system design and dispatch optimization, as well as system evaluation is proposed. For energy demand prediction, agent-based modeling is used to simulate the demand of electricity, heating, cooling, gas and fresh water for the case study community on the island. The k-means clustering and scenario tree are further adopted to generate representative stochastic scenarios, which are applied to capture the uncertainty of energy demand. A multi-objective optimization model is developed to optimize the system design and scheduling strategy simultaneously. In order to demonstrate the effectiveness of the proposed approach and to evaluate the obtained optimal solutions for the case study, different objectives and extreme weather conditions are specifically considered. The optimal solution obtained shows that compared to battery storage, a 2.5% annual cost reduction can be achieved by using power-to-gas technology for energy storage. The findings also suggest that extreme weather conditions can be coped with by increasing the capacity of biogas generation, desalination, and energy storage equipment, thereby improving the resilience of the island energy system.

Journal article

van de Berg D, Savage T, Petsagkourakis P, Zhang D, Shah N, del Rio-Chanona EAet 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.

Journal article

Gulliford MJS, Orlebar RH, Bird MH, Acha S, Shah Net 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.

Journal article

Sarabia Escriva EJ, Hart M, Acha Izquierdo S, Soto Frances V, Shah N, Markides Cet 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.

Journal article

Bernardi A, Sarkis M, Triantafyllou N, Lakelin M, Shah N, Papathanasiou MMet 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.

Journal article

Lin J, Zhong X, Wang J, Huang Y, Bai X, Wang X, Shah N, Xie S, Zhao Yet 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

Journal article

Ibrahim D, Kis Z, Tak K, Papathanasiou MM, Kontoravdi C, Chachuat B, Shah Net 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.

Journal article

Mac Dowell N, Sunny N, Brandon N, Herzog H, Ku AY, Maas W, Ramirez A, Reiner DM, Sant GN, Shah Net al., 2021, The hydrogen economy: A pragmatic path forward, Joule, Vol: 5, Pages: 2524-2529, ISSN: 2542-4351

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.

Journal article

Falugi P, O'Dwyer E, Kerrigan EC, Atam E, Zagorowska M, Strbac G, Shah N, Falugi Pet 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.

Conference paper

Luo H, Barrio J, Sunny N, Li A, Steier L, Shah N, Stephens IEL, Titirici M-Met 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

Journal article

Sarkis M, Bernardi A, Shah N, Papathanasiou MMet al., 2021, Decision support tools for next-generation vaccines and advanced therapy medicinal products: present and future, Current Opinion in Chemical Engineering, Vol: 32, Pages: 100689-100689, ISSN: 2211-3398

Advanced Therapy Medicinal Products (ATMPs) are a novel class of biological therapeutics that utilise ground-breaking clinical interventions to prevent and treat life-threatening diseases. At the same time, viral vector-based and RNA-based platforms introduce a new generation of vaccine manufacturing processes. Their clinical success has led to an unprecedented rise in the demand that, for ATMPs, leads to a predicted market size of USD 9.6 billion by 2026. This paper discusses how mathematical models can serve as tools to assist decision-making in development, manufacturing and distribution of these new product classes. Recent contributions in the space of process, techno-economic and supply chain modelling are highlighted. Lastly, we present and discuss how Process Systems Engineering can be further advanced to support commercialisation of advanced therapeutics and vaccines.

Journal article

Acha Izquierdo S, Shah N, Soler A, 2021, Best practices to mitigate CO2 operational emissions: A case study of the Basque Country energy ecosystem, Ekonomiaz Basque Economic Review, ISSN: 0213-3865

This work reviews the best practices to reduce CO2 emissions in energy intensive organizations and energy value-chains by highlighting the synergy that can be built with like-minded organizations via collaborations; taking the Basque Country as a case study. An academic review covers how corporate strategies are attempting to curtail emissions in a systematic manner. The study is then complimented by findings obtained from interviews of key stakeholders in the Basque Country responsible for playing an important role in implementing a green agenda. The interviews allow us to highlight flagship projects and assess the collaborative framework strengths and challenges. Results indicate that organizations are well underway in implementing and researching low carbon solutions, but issues surrounding governance, strategy, and regulatory challenges can slow progress of goals.

Journal article

van de Berg D, Kis Z, Behmer CF, Samnuan K, Blakney A, Kontoravdi K, Shattock R, Shah Net al., 2021, Quality by design modelling to support rapid RNA vaccine production against emerging infectious diseases, npj Vaccines, Vol: 6, ISSN: 2059-0105

Rapid-response vaccine production platform technologies, including RNA vaccines, are being developed to combat viral epidemics and pandemics. A key enabler of rapid response is having quality-oriented disease-agnostic manufacturing protocols ready ahead of outbreaks. We are the first to apply the Quality by Design (QbD) framework to enhance rapid-response RNA vaccine manufacturing against known and future viral pathogens. This QbD framework aims to support the development and consistent production of safe and efficacious RNA vaccines, integrating a novel qualitative methodology and a quantitative bioprocess model. The qualitative methodology identifies and assesses the direction, magnitude and shape of the impact of critical process parameters (CPPs) on critical quality attributes (CQAs). The mechanistic bioprocess model quantifies and maps the effect of four CPPs on the CQA of effective yield of RNA drug substance. Consequently, the first design space of an RNA vaccine synthesis bioreactor is obtained. The cost-yield optimization together with the probabilistic design space contribute towards automation of rapid-response, high-quality RNA vaccine production.

Journal article

Freire Ordonez D, Shah N, Guillen-Gosalbez G, 2021, Economic and full environmental assessment of electrofuels via electrolysis and co-electrolysis considering externalities, APPLIED ENERGY, Vol: 286, ISSN: 0306-2619

Journal article

Antonakoudis A, Kis Z, Kontoravdi K, Kotidis P, Papathanasiou M, Shah N, Tomba E, Varsakelis C, von Stoch Met al., 2021, Accelerating product and process development through a model centric approach, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc., Pages: 285-338, ISBN: 978-1-945584-22-0

Book chapter

Kis Z, Papathanasiou M, Kotidis P, Antonakoudis T, Kontoravdi K, Shah Net al., 2021, Stability modelling for biopharmaceutical process intermediates, Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development, Editors: Campa, Khan, Publisher: Parenteral Drug Association, Inc, Pages: 200-225, ISBN: 978-1-945584-22-0

Book chapter

Vallejo L, Mazur C, Strapasson A, Cockerill T, Gambhir A, Hills T, Jennings M, Jones O, Kalas N, Keirstead J, Khor C, Napp T, Tong D, Woods J, Shah Net al., 2021, Halving Global CO2 Emissions by 2050: Technologies and Costs, International Energy Journal, Vol: 21, Pages: 147-158, ISSN: 1513-718X

This study provides a whole-systems simulation on how to halve global CO2 emissions by 2050, compared to 2010, with an emphasis on technologies and costs, in order to avoid a dangerous increase in the global mean surface temperature by end the of this century. There still remains uncertainty as to how much a low-carbon energy system costs compared to a high-carbon system. Integrated assessment models (IAMs) show a large range of costs of mitigation towards the 2°C target, with up to an order of magnitude difference between the highest and lowest cost, depending on a number of factors including model structure, technology availability and costs, and the degree of feedback with the wider macro-economy. A simpler analysis potentially serves to highlight where costs fall and to what degree. Here we show that the additional cost of a low-carbon energy system is less than 1% of global GDP more than a system resulting from low mitigation effort. The proposed approach aligns with some previous IAMs and other projections discussed in the paper, whilst also providing a clearer and more detailed view of the world. Achieving this system by 2050, with CO2 emissions of about 15GtCO2, depends heavily on decarbonisation of the electricity sector to around 100gCO2/kWh, as well as on maximising energy efficiency potential across all sectors. This scenario would require a major mitigation effort in all the assessed world regions. However, in order to keep the global mean surface temperature increase below 1.5°C, it would be necessary to achieve net-zero emission by 2050, requiring a much further mitigation effort.

Journal article

Quek VC, Shah N, Chachuat B, 2021, Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening – Part I: Model development, Separation and Purification Technology, Vol: 258, Pages: 1-13, ISSN: 1383-5866

This paper presents a predictive mathematical model of high-pressure membrane contactor, with a view to developing a plant-wide model of natural gas sweetening including amine regeneration. We build upon an existing model of high-pressure membrane contactor by Quek et al. [Chem Eng Res Des 132:1005–1019, 2018], which uses a combination of 1-d and 2-d mass-balance equations to predict the CO2 absorption flux and membrane wetting under lean solvent operation. For the first time, quantitative predictions of the CO2 absorption flux can be made under both lean and semi-lean operations. A 1-d energy balance that accounts for the solvent evaporative losses and the exothermic CO2 absorption into the amine is solved alongside the mass-balance equations, in order to predict the solvent temperature profile inside the contactor. The evaporative losses of water and amines can be quantified separately, as well as the absorptive losses of light hydrocarbons with the amine solvent. The model’s predictive capability is tested against data from a lab-scale module and a pilot-scale module that is operated under industrially relevant conditions at a natural gas processing facility in Malaysia. A close agreement between model predictions and measurements of the CO2 absorption flux, solvent temperature profile, and hydrocarbon loss is observed for a wide range of gas and solvent flowrates and compositions, thereby validating the modeling assumptions. The contactor model is combined in a plant-wide model of natural gas sweetening in the companion paper, where it is used for process integration and analysis.

Journal article

Sarkis M, Bernardi A, Shah N, Papathanasiou MMet al., 2021, Emerging challenges and opportunities in pharmaceutical manufacturing and distribution, Processes, Vol: 9, Pages: 1-16, ISSN: 2227-9717

The rise of personalised and highly complex drug product profiles necessitates significant advancements in pharmaceutical manufacturing and distribution. Efforts to develop more agile, responsive, and reproducible manufacturing processes are being combined with the application of digital tools for seamless communication between process units, plants, and distribution nodes. In this paper, we discuss how novel therapeutics of high-specificity and sensitive nature are reshaping well-established paradigms in the pharmaceutical industry. We present an overview of recent research directions in pharmaceutical manufacturing and supply chain design and operations. We discuss topical challenges and opportunities related to small molecules and biologics, dividing the latter into patient- and non-specific. Lastly, we present the role of process systems engineering in generating decision-making tools to assist manufacturing and distribution strategies in the pharmaceutical sector and ultimately embrace the benefits of digitalised operations.

Journal article

Quek VC, Shah N, Chachuat B, 2021, Plant-wide assessment of high-pressure membrane contactors in natural gas sweetening – Part II: Process analysis, Separation and Purification Technology, Vol: 258, Pages: 1-11, ISSN: 1383-5866

This paper presents a model-based assessment of a natural gas sweetening process combining high-pressure membrane contactor with conventional amine regeneration. The analysis builds on a mathematical model of the membrane contactor developed in the companion paper, which is capable of quantitative predictions of the CO2 and hydrocarbon absorption in the amine solvent and the solvent evaporative losses to the treated gas. The predictive capability of the plant-wide model is tested against data from a pilot plant operated under industrially relevant conditions at a natural gas processing facility in Malaysia, showing a close agreement of the predictions with the CO2 outlet purity and the energy consumption at various CO2 loading in the amine solvent. This enables a model-based analysis of various operational decisions on the plant-wide solvent losses and hydrocarbon recovery from the rich amine. A new semi-lean process configuration that replaces the energy-intensive stripper column by a simple flash separator is shown to reduce the overall energy consumption significantly while still meeting sales gas specification. This new configuration forms the basis for the scale-up of a commercial natural gas sweetening process, which shows a high intensification potential in terms of volume footprint and energy duty compared to conventional amine treating plants.

Journal article

Kucherenko S, Klymenko O, Shah N, 2021, Application of Machine Learning and Global Sensitivity Analysis for Identification and Visualization of Design Space, Computer Aided Chemical Engineering, Pages: 875-881

The design space (DS) is defined as the combination of materials and process conditions which provides assurance of quality for a pharmaceutical. A model-based approach to identify a probability-based DS requires costly simulations across the entire process parameter space (certain) and the uncertain model parameter space (e.g. material properties). We demonstrate that application of metamodel-based filters and global sensitivity analysis (GSA) can significantly reduce model complexity and reduce computational time for identifying and quantifying DS. Once DS is identified it is necessary to present it graphically. The output of identification of DS is a multi-dimensional probability map. The projection of the multi-dimensional DS to a 2D representation is still unavoidable irrespectively of the method used to reach such probability mapping. We showed that application of constraint GSA can dramatically reduce the number of required for visualization 2D projections.

Book chapter

Freire Ordóñez D, Halfdanarson T, Ganzer C, Guillén-Gosálbez G, Dowell NM, Shah Net al., 2021, Carbon or Nitrogen-based e-fuels? A comparative techno-economic and full environmental assessment, Computer Aided Chemical Engineering, Pages: 1623-1628

The increasing energy demand for mobility services and the growing concern about global warming have become significant drivers for these services’ decarbonisation. In this regard, the production and use of fuels obtained from just water, air and renewable energies instead of conventional fossil fuels have caught much attention within the research community. Recently, nitrogen-based e-fuels have been praised for their potential to satisfy mobility and transportation services with a reduced carbon footprint compared to their carbon-based analogues, given their carbon-neutral nature. To evaluate this hypothesis, we conducted a location-based, techno-economic and cradle-to-grave environmental assessment for solar methanol (MeOH) and ammonia (NH3) based on an optimisation model. Methanol and ammonia were considered for this study due to their relative ease of manufacture and lower production costs than complex fuels, e.g., FT-fuels, and the growing interest in using them as transportation fuels. From this analysis, we concluded that ammonia could have similar production costs, ca., 300 USD/GJ, but better environmental performance than methanol regarding global warming potential (GWP) and the three endpoint impact categories of the ReciPe 2016 LCA damage model, i.e., human health, ecosystems and resources. These results are highly dependent on the hydrogen storage options available; their costs and carbon footprints.

Book chapter

Kusumo KP, Kuriyan K, García-Muñoz S, Shah N, Chachuat Bet al., 2021, Continuous-Effort Approach to Model-Based Experimental Designs, Computer Aided Chemical Engineering, Pages: 867-873

Model-based design of experiments is a technique for accelerating the development of mathematical models. Through maximally informative experiments, time and resources for estimating uncertain model parameters are minimized. This article presents a method for computing effort-based experimental designs, whereby designs are akin to experimental recipes. As well as identifying which experiments are the most informative, the optimal experimental effort to dedicate to each experiment is also optimized. Upon discretizing the experimental design space and treating the efforts as continuous decision variables, this method leads to convex optimization problems regardless of the model structure, which is ideal for large, parallel experimental campaigns. The case study of a batch reactor model with four parameters is presented to illustrate the methodology.

Book chapter

Kusumo KP, Morrissey J, Mitchell H, Shah N, Chachuat Bet 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

Conference paper

Al-Qahtani A, Parkinson B, Hellgardt K, Shah N, Guillen-Gosalbez Get al., 2021, Uncovering the true cost of hydrogen production routes using life cycle monetisation, Applied Energy, Vol: 281, Pages: 115958-115958, ISSN: 0306-2619

Hydrogen has been identified as a potential energy vector to decarbonise the transport and chemical sectors and achieve global greenhouse gas reduction targets. Despite ongoing efforts, hydrogen technologies are often assessed focusing on their global warming potential while overlooking other impacts, or at most including additional metrics that are not easily interpretable. Herein, a wide range of alternative technologies have been assessed to determine the total cost of hydrogen production by coupling life-cycle assessments with an economic evaluation of the environmental externalities of production. By including monetised values of environmental impacts on human health, ecosystem quality, and resources on top of the levelised cost of hydrogen production, an estimation of the “real” total cost of hydrogen was obtained to transparently rank the alternative technologies. The study herein covers steam methane reforming (SMR), coal and biomass gasification, methane pyrolysis, and electrolysis from renewable and nuclear technologies. Monetised externalities are found to represent a significant percentage of the total cost, ultimately altering the standard ranking of technologies. SMR coupled with carbon capture and storage emerges as the cheapest option, followed by methane pyrolysis, and water electrolysis from wind and nuclear. The obtained results identify the “real” ranges for the cost of hydrogen compared to SMR (business as usual) by including environmental externalities, thereby helping to pinpoint critical barriers for emerging and competing technologies to SMR.

Journal article

Bernardi A, Papathanasiou M, Lakelin MW, Shah Net al., 2021, Assessment of intermediate storage and distribution nodes in personalised medicine, Computer Aided Chemical Engineering, Pages: 1997-2002

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 option upstream of the network as means of: (a) debottlenecking manufacturing lines and (b) increasing facility utilisation. In this setting, we assess cost-effectiveness and flexibility of a decentralised supply chain and we evaluate network performance with respect to two key performance indicators (KPIs): (a) average production cost and (b) average response treatment time. The trade-off between cost-efficiency and responsiveness is examined and discussed.

Book chapter

Kis Z, Kontoravdi C, Shattock R, Shah Net al., 2020, Resources, production scales and time required for producing RNA vaccines for the global pandemic demand., Vaccines (Basel), Vol: 9, Pages: 1-14, ISSN: 2076-393X

To overcome pandemics, such as COVID-19, vaccines are urgently needed at very high volumes. Here we assess the techno-economic feasibility of producing RNA vaccines for the demand associated with a global vaccination campaign. Production process performance is assessed for three messenger RNA (mRNA) and one self-amplifying RNA (saRNA) vaccines, all currently under clinical development, as well as for a hypothetical next-generation saRNA vaccine. The impact of key process design and operation uncertainties on the performance of the production process was assessed. The RNA vaccine drug substance (DS) production rates, volumes and costs are mostly impacted by the RNA amount per vaccine dose and to a lesser extent by the scale and titre in the production process. The resources, production scale and speed required to meet global demand vary substantially in function of the RNA amount per dose. For lower dose saRNA vaccines, global demand can be met using a production process at a scale of below 10 L bioreactor working volume. Consequently, these small-scale processes require a low amount of resources to set up and operate. RNA DS production can be faster than fill-to-finish into multidose vials; hence the latter may constitute a bottleneck.

Journal article

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