This work develops a model- and optimisation-based methodologies that address challenges related to the production of low-ILUC biomass feedstocks. The proposed methodology starts by collecting all relevant information required, followed by the development of decision-support models, and finally the developed models are used to investigate and analyse four case studies, i.e., planning of Iow-ILUC biomass feedstock production, biomethane production using low-ILUC biomass feedstocks, integrated production of 1G and 2G bioethanol using Miscanthus, and production of HVO using castor seeds. Analysis of results show that farmers interested in these models are recommended to sell low-ILUC biomass such as soybean, wheat and brassica above the breakeven price to avoid losses. The estimated selling price for the three crops are 362 €/t, 321 €/t and 381 €/t respectively. To meet the demand of 40,000 t/yr of 2G bioethanol in the UK, approximately 17,094 hectares of underutilised land is required.

The simultaneous administration of SARS-CoV-2 and influenza vaccines is being carried out for the first time in the UK and around the globe in order to mitigate the health, economic, and societal impacts of these respiratory tract diseases. However, a systematic approach for planning the vaccine distribution and administration aspects of the vaccination campaigns would be beneficial. This work develops a novel multi-product mixed-integer linear programming (MILP) vaccine supply chain model that can be used to plan and optimise the simultaneous distribution and administration of SARS-CoV-2 and influenza vaccines. The outcomes from this study reveal that the total budget required to successfully accomplish the SARS-CoV-2 and influenza vaccination campaigns is equivalent to USD 7.29 billion, of which the procurement costs of SARS-CoV-2 and influenza vaccines correspond to USD 2.1 billion and USD 0.83 billion, respectively. The logistics cost is equivalent to USD 3.45 billion, and the costs of vaccinating individuals, quality control checks, and vaccine shipper and dry ice correspond to USD 1.66, 0.066, and 0.014, respectively. The analysis of the results shows that the choice of rolling out the SARS-CoV-2 vaccine during the vaccination campaign can have a significant impact not only on the total vaccination cost but also on vaccine wastage rate.

Biofuels derived from biomass feedstocks produced following the implementation of measures that avoid indirect land used change (ILUC) have the potential of reducing dependency on fossil-based fuels without competing with food value chain. This work develops a model- and optimisation-based methodologies that address challenges related to the production of low-ILUC biomass feedstocks. Case studies investigated include planning of Iow-ILUC biomass feedstock production, biomethane production using low-ILUC biomass feedstocks, integrated production of first generation (1G) and second generation (2G) bioethanol using Miscanthus, and production of hydrotreated vegetable oil (HVO) using castor seeds. Analysis of results show that farmers interested in these models are recommended to sell low-ILUC biomass such as soybean, wheat and brassica above the breakeven price to avoid losses. The estimated selling price for the three crops are 362 /t, 321 /t and 381 /t respectively. To meet the demand of 40,000 t/yr of 2G bioethanol in the UK, approximately 17,094 ha of underutilised land is required. Policy makers should consider options to support alternatives such as retrofitting, and inter-cropping to avoid or mitigate ILUC.

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.

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.

Sustainable biofuels are an important tool for the decarbonisation of transport. This is especially true in aviation, maritime, and heavy-duty sectors with limited short-term alternatives. Their use by conventional transport fleets requires few changes to the existing infrastructure and engines, and thus their integration can be smooth and relatively rapid. Provision of feedstock should comply with sustainability principles for (i) producing additional biomass without distorting food and feed markets and (ii) addressing challenges for ecosystem services, including biodiversity, and soil quality. This paper performs a meta-analysis of current research for low indirect land use change (ILUC) risk biomass crops for sustainable biofuels that benefited either from improved agricultural practices or from cultivation in unused, abandoned, or severely degraded land. Two categories of biomass crops are considered here: oil and lignocellulosic. The findings confirm that there are significant opportunities to cultivate these crops in European agro-ecological zones with sustainable agronomic practices both in farming land and in land with natural constraints (unused, abandoned, and degraded land). These could produce additional low environmental impact feedstocks for biofuels and deliver economic benefits to farmers.

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 quantified 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 epidemic/pandemic preparedness, given the disease-agnostic nature of these vaccine production platforms.

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.

The need for petroleum refineries to process different types of crude oil in order to maximise profit margin and to meet demand for products, calls for flexibility in the design and optimisation of crude oil distillation systems comprising distillation units and the heat recovery network. Crude oil distillation is a complex, capital- and energy-intensive process. The large number of degrees of freedom (column structure and operating conditions) and complex interactions within the system make the design and optimisation of crude oil distillation system a highly challenging task. This work develops new methodologies for the design of crude oil distillation systems that process a single crude oil feedstock and multiple crude oil feedstocks. In this work, the crude oil distillation unit is modelled using a rigorous tray-by-tray model where the number of trays active in each section is also a design degree of freedom. The model is embedded in an optimisation framework, together with a heat recovery model (applying pinch analysis), for design of an energy-efficient and cost-effective distillation system. The optimisation framework addresses both structural and operational degrees of freedom of the system, capturing the trade-off between capital and energy costs, and accounting for heat integration. The distillation model is built in Aspen HYSYS, while the optimisation is carried out in MatLab using a genetic algorithm, where data is exchanged during process simulation and optimisation. To overcome the shortcomings of the rigorous distillation model in the context of system optimisation, surrogate models based on artificial neural networks (ANN) and a support vector machine (SVM) are developed and applied in the optimisation framework. The ANN model simulates the crude oil distillation unit, while the SVM partitions the search space, increasing the likelihood that the optimised solution will converge when simulated using a rigorous model. The SVM helps to reduce computational effort by focusing the search on potentially feasible solutions. Both the ANN and SVM are fitted to results of multiple rigorous simulations of the distillation unit. The proposed surrogate modelling approach is extended to take into account multiple crude oil feedstocks in the design of the distillation unit. The distillation column models for multiple crude oils and heat recovery model are embedded in a two-stage optimisation framework, in which a hybrid stochastic-deterministic approach is applied to optimise structural variables and distillation column operating conditions. The overall objective is to maximise net profit while meeting product quality (and flow rate) constraints. The capabilities of the proposed methodologies are illustrated using industrially-relevant case studies. Results indicate that the used of surrogate model instead of rigorous models reduces computational time without compromising solution accuracy and optimality. The design approach to account for flexible operation is shown to identify effectively design alternatives that…

Designing feasible and economically-viable organic Rankine cycle (ORC) systems for applications such as high-grade heat recovery from the exhaust gases (400-600 °C) of stationary internal combustion engines (ICEs) has two main challenges: (i) selecting and designing an appropriate expansion technology, amongst the other system components, and (ii) selecting the optimal working fluid, and operational system parameters. In this work, comprehensive component models are integrated into an ORC system model to evaluate the onand off-design performance of a 2.5-MWe ORC engine using either a reciprocatingpiston expander or a radial-inflow turbine. The performance of the reciprocating-piston expander is predicted using a dynamic lumped-mass model, and a one-dimensional model based on the mean-line method is used to predict the performance of the turbine. An initial working-fluid screening leads to R1233zd being selected for further consideration, given the minimal specific investment costs and lowpressure ratio of the resulting ORC systems, thus assisting the design of suitable expansion devices. The approximate design point obtained from the screening study is used to obtain optimised piston and turbine designs that are then used to produce fulland part-load performance maps that are integrated into the ORC system model. The ORC engine with a turbine is found to deliver 127 kW at full load, at a specific investment cost of 1660 £/kW, while the piston expander produces a lower net power of 68 kW, but at a lower cost of 1250 £/kW, while also showing greater robustness to variations in the heat-source conditions.

The achievement of economical designs of organic Rankine cycle (ORC) systems for deployment in a variety of applications can be greatly facilitated by computer-aided molecular design (CAMD) techniques, which require accurate and reliable group contribution ( GC) equations of state (EoS) to predict the thermodynamic and transport properties of suitable and optimal working fluids. In this work, the capabilities of SAFT-gamma Mie, a GC EoS, is extended to include functional groups such as -CF3 and >CF2, that can allow the design of environmentally-friendly refrigerant working fluids. The parameters of these groups in the SAFT-gamma Mie EoS are regressed from experimental data. With these parameters, maximum average absolute deviations (AADs) from experimental data of 5 % for the vapour pressure, 3 % for the saturated liquid density and 3 % for the liquid-phase density are reported, confirming the suitability of this EoS for working-fluid thermodynamic property predictions. Furthermore, by employing suitable GC methods, important transport properties such as the thermal conductivity, liquid viscosity and vapour viscosity are predicted with AADs of 28 %, 23 % and 6 %, respectively. The new GC parameters are implemented in an existing CAMD-ORC model and parametric optimization studies are performed. For a relatively low-temperature (150 degrees C) heat source, the employment of refrigerant heptafluorobutane results in a net power output of 43 kW, which outperforms the best performing hydrocarbon (n-propane, 35 kW). This confirms the need to include refrigerants within such ORC frameworks in addition to hydrocarbons. At higher heat-source temperatures of 250 degrees C and 350 degrees C, the optimal working fluids are 2-pentene and 2-hexene, with corresponding maximum power outputs of 137 kW and 219 kW.

This paper introduces a novel optimization-based framework for the design of a crude oil distillation unit. The approach presented integrates surrogate models based on artificial neural networks (ANN) with feasibility constraints generated using a support vector machine (SVM) in order to optimise the column configuration and its operating conditions. The SVM filters infeasible design options from the solution space of the design problem, which reduces the computational effort and ultimately improves the quality of the final solution. Rigorous process simulations are used to build the surrogate model, while pinch analysis is employed to determine the maximum heat recovery and minimum utility costs. The objective is to minimise the total annualized cost, which is optimised by combining a genetic algorithm with the surrogate model. The approach is illustrated in an industrially relevant case study.

Crude oil distillation systems, comprising distillation units and their associated heat recovery networks, are highly complex and integrated systems. Their function is to separate crude oil into several streams with different boiling ranges for downstream processing. In practice, these systems typically need to be operated efficiently, so that the value added by the separation units is maximized (e.g. by maximizing flows of the most valuable intermediate products while minimizing production costs). Process improvement projects typically seek to increase production and/or to reduce energy consumption in existing crude oil distillation systems. Recent developments in design and operational optimization of crude oil distillation systems apply surrogate models, together with stochastic optimization techniques, for column design or operational optimization. Column operation is highly constrained by the product specifications and, in existing columns, by physical limitations related to column configuration and size. Column models must capture these constraints. The effectiveness of surrogate modelling of the columns is enhanced by this work that develops complementary screening and filtering correlations and surrogate models (using artificial neural networks and support vector machines) to define feasibility bounds. Applying these feasibility bounds enables more targeted searches, bringing robustness and efficiency to the optimization frameworks. Examples and case studies illustrate the effectiveness of the correlations and surrogate models for defining constraints in design and operational optimization approaches.

The complex nature of crude oil distillation units, including their interactions with the associated heat recovery network and the large number of degrees of freedom, makes their optimization a very challenging task. We address here the design of a complex crude oil distillation unit by integrating rigorous tray-by-tray column simulation using commercial process simulation software with an optimization algorithm. While several approaches were proposed to tackle this problem, most of them relied on simplified models that are unable to dear with the whole complexity of the problem. The design problem is herein formulated to consider both structural variables (the number of trays in each column section) and operational variables (feed inlet temperature, pump-around duties and temperature drops, stripping steam flow rates and reflux ratio). A simulation-optimization approach for designing such a complex system is applied, which searches for the best design while accounting for heat recovery opportunities using pinch analysis. The approach is illustrated by its application to a specific distillation unit, in which numerical results demonstrate that the new approach is capable of identifying appealing design options while accounting for industrially relevant constraints.