Publications
677 results found
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.
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.
van de Berg D, Petsagkourakis P, Shah N, et al., 2022, Data-driven coordination of expensive black-boxes, Computer Aided Chemical Engineering, Pages: 1159-1164
Coordinating 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.
Triantafyllou 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: 2197-2202
Autologous 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.
Yliruka 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: 2035-2040
In 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.
Soh QY, O'Dwyer E, Acha S, et al., 2022, Model agnostic framework for analyzing rainwater harvesting system behaviors, Computer Aided Chemical Engineering, Pages: 2023-2028
To 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.
Kis 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: 2167-2172
Vaccine 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.
Triantafyllou N, Bernardi A, Lakelin M, et al., 2022, Fresh vs frozen: assessing the impact of cryopreservation in personalised medicine, Computer Aided Chemical Engineering, Pages: 955-960
Chimeric 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.
Sarkis M, Tak K, Chachuat B, et al., 2022, Towards Resilience in Next-Generation Vaccines and Therapeutics Supply Chains, Computer Aided Chemical Engineering, Pages: 931-936
Recent 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.
Ibrahim 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: 1633-1638
This 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.
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.
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.
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.
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.
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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- Citations: 10
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.
Luo 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
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- Citations: 130
Mac Dowell N, Sunny N, Brandon N, et 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.
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.
O’Dwyer E, Atam E, Falugi P, et al., 2021, A modelling workflow for predictive control in residential buildings, Active Building Energy Systems, Editors: Doyle, Publisher: Springer International Publishing, Pages: 99-128, ISBN: 9783030797416
Despite a large body of research, the widespread application of Model Predictive Control (MPC) to residential buildings has yet to be realised. The modelling challenge is often cited as a significant obstacle. This chapter establishes a systematic workflow, from detailed simulation model development to control-oriented model generation to act as a guide for practitioners in the residential sector. The workflow begins with physics-based modelling methods for analysis and evaluation. Following this, model-based and data-driven techniques for developing low-complexity, control-oriented models are outlined. Through sections detailing these different stages, a case study is constructed, concluding with a final section in which MPC strategies based on the proposed methods are evaluated, with a price-aware formulation producing a reduction in operational space-heating cost of 11%. The combination of simulation model development, control design and analysis in a single workflow can encourage a more rapid uptake of MPC in the sector.
Sarkis M, Bernardi A, Shah N, et 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.
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, Vol: 99, 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.
van de Berg D, Kis Z, Behmer CF, et 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.
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, Pages: 1-21, ISSN: 0306-2619
Electrofuels from CO2 and H2O have recently emerged as a promising alternative to reduce the carbon footprint of fossil fuels, yet their full economic and environmental performance remains unclear. Here, the production of renewable petrol from electrolysis and co-electrolysis-based processes is critically assessed, combining a palette of tools encompassing process simulation, costing evaluation, life-cycle assessment, and uncertainty analysis. Our results show that electrofuels are currently very expensive (10.4-fold higher cost compared to petrol), even when considering externalities (indirect cost of environmental impacts). Electrofuels could become cheaper than the fossil analogue, yet this would require relying on low-cost renewable electricity, which may find alternative uses. From an environmental perspective, we found that despite reducing the carbon footprint of the fossil counterpart, electrofuels could exacerbate impacts on human health due to burden-shifting. Overall, our work highlights the need to embrace impacts beyond climate change to ensure a comprehensive assessment of alternative fuels, and to monetise them to underpin a fair comparison with the fossil analogue.
Antonakoudis A, Kis Z, Kontoravdi K, et 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
Kis Z, Papathanasiou M, Kotidis P, et 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
Vallejo L, Mazur C, Strapasson A, et 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.
Sarkis M, Bernardi A, Shah N, et 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.
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