Imperial College London

ProfessorNilayShah

Faculty of EngineeringDepartment of Chemical Engineering

Head of Department of Chemical Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6621n.shah

 
 
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Assistant

 

Miss Nazma Mojid +44 (0)20 7594 3918

 
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Location

 

ACEX 304/5ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

525 results found

O'Dwyer E, Chen K, Wang H, Wang A, Shah N, Guo Met al., 2020, Optimisation of wastewater treatment strategies in eco-industrial parks: technology, location and transport, Chemical Engineering Journal, Vol: 381, Pages: 1-12, ISSN: 1385-8947

The expanding population and rapid urbanisation, in particular in the Global South, areleading to global challenges on resource supply stress and rising waste generation. A transformation to resource-circular systems and sustainable recovery of carbon-containing andnutrient-rich waste offers a way to tackle such challenges. Eco-industrial parks have thepotential to capture symbioses across individual waste producers, leading to more effectivewaste-recovery schemes. With whole-system design, economically attractive approaches canbe achieved, reducing the environmental impacts while increasing the recovery of high-valueresources. In this paper, an optimisation framework is developed to enable such design,allowing for wide ranging treatment options to be considered capturing both technologicaland financial detail. As well as technology selection, the framework also accounts for spatial aspects, with the design of suitable transport networks playing a key role. A range ofscenarios are investigated using the network, highlighting the multi-faceted nature of theproblem. The need to incorporate the impact of resource recovery at the design stage isshown to be of particular importance.

Journal article

Kucherenko S, Giamalakis D, Shah N, García-Muñoz Set al., 2020, Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling, Computers & Chemical Engineering, Vol: 132, Pages: 1-9, ISSN: 0098-1354

The design space (DS) is defined as the combination of materials and process conditions which provides assurance of quality for a pharmaceutical product (e.g. purity, potency, uniformity). A model-based approach to identify a probability-based design space requires simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space if explicitly considered by the model. This exercise is a demanding task. A novel theoretical and numerical framework for determining probabilistic DS using metamodelling and adaptive sampling is developed. Several approaches were proposed and tested among which the most efficient is a new multi-step adaptive technique based using a metamodel for a probability map as an acceptance-rejection criterion to optimize sampling to identify the DS. It is shown that application of metamodel-based filters can significantly reduce model complexity and computational costs with speed up of two orders of magnitude observed here.

Journal article

Liu S, Papageorgiou LG, Shah N, 2020, Optimal design of low-cost supply chain networks on the benefits of new product formulations, Computers and Industrial Engineering, Vol: 139, ISSN: 0360-8352

© 2019 Elsevier Ltd Formulated products usually comprise a high amount of low-cost ingredients, e.g., water, which could be removed by concentration, and the resulting concentrated products could generate economic advantages, especially in long-distance transportation. This work examines the economic benefits of new product formulations resulted from a new process and product design technology through the optimal design of low-cost formulated product supply chain networks for different product formulations, including traditional formulations and new formulations via concentration. Based on mixed-integer linear programming techniques, an optimisation-based framework is proposed to determine the optimal locations and capacities of plants, warehouses, and distribution centres, as well as the production and distribution planning decisions, by minimising the unit total cost, including raw material, packaging, conversion, inventory, transportation and depreciation costs. In order to deal with the computational complexity, a tailored hierarchical solution approach is developed, in which facility locations and connections are determined by an aggregated static model, and a reduced dynamic model is then solved to determine the facility capacities and the production amounts, distribution flows, and inventory levels in each time period. A case study of a fast-moving consumer goods supply chain is investigated to demonstrate the economic benefits of new product formulations by implementing and comparing different production and distribution structures. The computational results from scenario and sensitivity analysis show that the manufacturing of final products, using a simple concept based on intermediate concentrated formulations produced at a centralised location, results in large supply chain benefits of an economic nature.

Journal article

Iruretagoyena D, Bikane K, Sunny N, Lu H, Kazarian SG, Chadwick D, Pini R, Shah Net al., 2020, Enhanced selective adsorption desulfurization on CO2 and steam treated activated carbons: Equilibria and kinetics, Chemical Engineering Journal, Vol: 379, Pages: 1-11, ISSN: 1385-8947

Activated carbons (ACs) show great potential for selective adsorption removal of sulfur (SARS) from hydrocarbon fuels but require improvements in uptake and selectivity. Moreover, systematic equilibria and kinetic analyses of ACs for desulfurization are still lacking. This work examines the influence of modifying a commercial-grade activated carbon (AC) by CO2 and steam treatment for the selective adsorption removal of dibenzothiophene (DBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT) at 323 K. An untreated AC and a charcoal Norit carbon (CN) were used for comparative purposes. Physicochemical characterization of the samples was carried out by combining N2-physisorption, X-ray diffractometry, microscopy, thermogravimetric and infrared analyses. The steam and CO2 treated ACs exhibited higher sulfur uptakes than the untreated AC and CN samples. The steam treated AC appears to be especially effective to remove sulfur, showing a remarkable sulfur uptake (~24 mgS·gads−1 from a mixture of 1500 ppmw of DBT and 1500 ppm 4,6-DMDBT) due to an increased surface area and microporosity. The modified ACs showed similar capacities for both DBT and the sterically hindered 4,6-DMDBT molecules. In addition, they were found to be selective in the presence of sulfur-free aromatics and showed good multicycle stability. Compared to other adsorbents, the modified ACs exhibited relatively high adsorption capacities. The combination of batch and fixed bed measurements revealed that the adsorption sites of the samples are characterized as heterogeneous due to the better fit to the Freundlich isotherm. The kinetic breakthrough profiles were described by the linear driving force (LDF) model.

Journal article

Thaore VB, Armstrong RD, Hutchings GJ, Knight DW, Chadwick D, Shah Net al., 2020, Sustainable production of glucaric acid from corn stover via glucose oxidation: An assessment of homogeneous and heterogeneous catalytic oxidation production routes, Chemical Engineering Research and Design, Vol: 153, Pages: 337-349, ISSN: 0263-8762

Journal article

Kong Q, Kuriyan K, Shah N, Guo Met al., 2019, Development of a responsive optimisation framework for decision-making in precision agriculture, Computers and Chemical Engineering, Vol: 131, ISSN: 0098-1354

Emerging digital technologies and data advances (e.g. smart machinery, remote sensing) not only enable Agriculture 4.0 to envisage interconnected agro-ecosystems and precision agriculture but also demand responsive decision-making. This study presents a mathematical optimisation model to bring real-time data and information to precision decision-support and to optimise short-term farming operation. To achieve responsive decision-support, we proposed two meta-heuristic algorithms i.e. a tailored genetic algorithm and a hybrid genetic-tabu search algorithm for solving the deterministic optimisation. The developed responsive optimisation framework has been applied to a hypothetical case study to optimise sugarcane harvesting in the KwaZulu Natal region in South Africa. In comparison with the optimal solutions derived from the exact algorithm, the proposed meta-heuristic methods lead to near optimal solutions (less than 5% from optimality) and significantly reduced computational time by over 95%. Our results suggest that the tailored genetic algorithm enables rapid solution searching but the solution quality on sugarcane harvesting cannot compete with the exact method. The hybrid genetic-tabu search algorithm achieved a good trade-off between computational time reduction and solution optimality, demonstrating the potential to enhance responsive decision making in precision sugarcane farming. Our research highlights the development of the responsive optimisation framework combining mixed integer linear programming and hybrid meta-heuristic search algorithms and its applications in real-time decision-making under Agriculture 4.0 vision.

Journal article

Bahzad H, Katayama K, Boot-Handford ME, Mac Dowell N, Shah N, Fennell PSet al., 2019, Iron-based chemical-looping technology for decarbonising iron and steel production, International Journal of Greenhouse Gas Control, Vol: 91, ISSN: 1750-5836

© 2019 The application of iron-based chemical-looping processes offers an efficient and convenient strategy for decarbonising iron and steel production. Here we present a novel chemical-looping with water splitting process for the co-generation of hydrogen and a saleable, reduced iron product (CLWSFe). The high-purity H2 stream provides a decarbonised fuel source for producing direct reduced iron (DRI), and the spent oxygen carrier, OC (if removed in reduced form) is a source of iron that could be blended with the DRI for casting or further processing to steel. A fully heat integrated model of the CLWSFe process developed in ASPEN-PLUS is presented. The thermal and exergy efficiencies of the optimised process were studied and compared with a conventional steam-methane reforming (SMR) process. An assessment of the economic feasibility based on CAPEX, OPEX and the production cost of hydrogen was carried out. The added value associated with the reduced iron (spent OC) product and its effect on the process CAPEX and OPEX was considered. The effective efficiency of the CLWSFe process was 20.8% higher than a conventional SMR process with the advantage of producing a saleable Fe product. The hydrogen production cost was 1.16 $/ kg-H2. The multicycle performance of different iron ores and steel production residues supplied by Nippon Steel Corporation were also studied in a thermogravimetric analyser at conditions relevant to both conventional chemical-looping combustion and CLWS processes. Kinetic and cyclic performance data provided useful inputs for the model assisting with reactor sizing and the estimation of oxygen carrier replenishment rates.

Journal article

Pozo C, Limleamthong P, Guo Y, Green T, Shah N, Acha S, Sawas A, Wu C, Siegert M, Guillén-Gosálbez Get al., 2019, Temporal sustainability efficiency analysis of urban areas via Data Envelopment Analysis and the hypervolume indicator: Application to London boroughs, Journal of Cleaner Production, Vol: 239, Pages: 1-14, ISSN: 0959-6526

Transitioning towards a more sustainable society calls for systematic tools to assess the sustainability performance of urban systems. To perform this task effectively, this work introduces a novel method based on the combined use of Data Envelopment Analysis (DEA) and the hypervolume indicator. In essence, DEA is applied to (i) distinguish between efficient and inefficient urban systems through the identification of best practices; and to (ii) establish improvement targets for the inefficient urban systems that, if attained, would make them efficient. Meanwhile, the hypervolume indicator is employed in conjunction with DEA to evaluate how urban systems evolve with time. The capabilities of this approach are illustrated through its application to the sustainability assessment of London boroughs between 2012–2014. Results reveal that most boroughs tend to perform well in terms of the indicators selected, with 20–25 of the 32 boroughs found efficient depending on the year. Regarding the temporal assessment, a global improvement in sustainability performance was found, with a strong relationship between the boroughs’ performances and their locations. The method proposed opens new pathways of social and environmental research for the application of advanced multi-criteria decision-support tools in the assessment and optimisation of urban systems.

Journal article

Gonzato S, Chimento J, ODwyer E, Bustos-Turu G, Acha S, Shah Net al., 2019, Hierarchical price coordination of heat pumps in a building network controlled using model predictive control, Energy and Buildings, Vol: 202, ISSN: 0378-7788

Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time.

Journal article

Cooper N, Panteli A, Shah N, 2019, Linear estimators of biomass yield maps for improved biomass supply chain optimisation, Applied Energy, Vol: 253, ISSN: 0306-2619

© 2019 Given the need to shift away from fossil fuels, expanding the role of the bioeconomy is vitally important. Biomass supply chain optimisation is a tool that has been used to help the biomass industry gain a foothold. Biomass supply chain models frequently use the average biomass yield of large areas to calculate overall yield. However, there can be large variation in the biomass yield within those areas, losing useful information. A biomass supply chain optimisation framework has been developed which uses information about the quality of land available by incorporating piecewise linear approximation of the biomass yield distribution into the optimisation. Linear approximations of the biomass yield variability allows the supply chain optimisation model to make more accurate decisions about quantity and location of biomass growth operations, affecting all downstream decisions. A case study of southwest Hungary for potential biomass industry viability has been examined using the framework to illustrate the impact of this yield information in the optimisation. The proposed framework successfully optimised the supply chain while accounting for variability in a spatially distributed resource, found that using the biomass yield estimates reduced the overall land usage by up to 17% in some cases, and improved biomass production by over 7%. Further, it improved biomass output, increasing the quantity of bioproducts which can be produced, and increasing the financial performance, thus demonstrating the importance of including yield variability in the optimisation. This framework could be used for other spatially distributed resources, such as solar insolation or wind availability.

Journal article

Escriva EJS, Acha S, LeBrun N, Francés VS, Ojer JMP, Markides CN, Shah Net al., 2019, Modelling of a real CO2 booster installation and evaluation of control strategies for heat recovery applications in supermarkets, International Journal of Refrigeration, Vol: 107, Pages: 288-300, ISSN: 0140-7007

This paper compares and quantifies the energy, environmental and economic benefits of various control strategies recovering heat from a CO2 booster system in a supermarket for space heating with the purpose of understanding its potential for displacing natural gas fuelled boilers. A theoretical steady-state model that simulates the behaviour of the CO2 system is developed and validated against field measurements obtained from an existing refrigeration system in a food-retail building located in the United Kingdom. Five heat recovery strategies are analysed by modifying the mass flows and pressure levels in the condenser. The model shows that a reduction of 48% in natural-gas consumption is feasible by the installation of a de-superheater and without any advanced operating strategy. However, the CO2 system can fully supply the entire space-heating requirement by adopting alternative control strategies, albeit by penalising the coefficient of performance (COP) of the compressor. Results show that the best energy strategy can reduce total consumption by 32%, while the best economic strategy can reduce costs by 6%. Findings from this work suggest that heat recovery systems can bring substantial benefits to improve the overall efficiency of energy-intensive buildings; although trade-offs need to be carefully considered and further analysed before embarking on such initiatives.

Journal article

Cumicheo C, Mac Dowell N, Shah N, 2019, Natural gas and BECCS: A comparative analysis of alternative configurations for negative emissions power generation, International Journal of Greenhouse Gas Control, Vol: 90, Pages: 1-11, ISSN: 1750-5836

There is a reliance on negative emissions technologies (NETs), primarily in the form of Bioenergy with Carbon Capture and Storage (BECCS) in most Integrated Assessment Model (IAM) scenarios which are capable of limiting the maximum global temperature rise to 1.5–2 °C. Two currently independent features of transition pathways are fuel switching from a coal to gas, and the deployment of BECCS. The former makes natural gas an important transition fuel which at the same time could be combined with biomass to further abate emissions. To date the majority of studies have considered BECCS in the context of a conversion from coal-fired base configuration. There is therefore a pressing need to identify routes for the effective utilization of biomass-derived fuels in the context of gas-fired power generation infrastructure. In this contribution, we study three distinct CCS-based processes which combine natural gas and biomass capable of producing low-, or carbon-negative power. Both fuel supply chains are considered in order to quantify the net overall CO2 emissions. An important insight is the configuration-specific impact of biomass co-combustion on the overall carbon intensity of power generated. We found that an external biomass combustion configuration was the most carbon negative, removing between 0.5–1 ton of CO2 per MWh of power generated. Results revealed a trade-off between carbon negativity and efficiency of the processes. The generation of net carbon negative power is observed to be highly sensitive to the carbon footprint of the biomass supply chain.

Journal article

Miu LM, Mazur CM, Van Dam KH, Lambert RSC, Hawkes A, Shah Net al., 2019, Going smart, staying confused: perceptions and use of smart thermostats in British homes, Energy Research and Social Science, Vol: 57, ISSN: 2214-6296

Given the significant contribution of housing to energy consumption, research into how residents use energy-saving technologies has been gathering pace. In this study, we investigate the perception and use of domestic smart heating controls by a small group of residents in London, UK. These residents are supplied by a district heat network (DHN) through underfloor heating systems, and took part in a trial where their controls were upgraded from traditional thermostats to smart thermostats. Pre- and post-trial interviews were used to assess changes in how residents interacted with and perceived their controls and heating systems. After the upgrade, more residents were satisfied with the usability of their controls and programmed heating schedules which matched their actual occupancy patterns, but they also made ad-hoc temperature and schedule adjustments more frequently. These changes provide insight into how a unique sample of residents, “twice removed” from the most intuitive methods of heating control, adjusted their behaviour and perceptions following a technology upgrade. Although the small sample size and lack of long-term monitoring limits the generalizability of our results, the findings open avenues for further research into whether smart heating controls change user behaviour in a way that improves the predictability of heating demand, a crucial aspect of improving DHN operation and reducing related emissions.

Journal article

Jing R, Wang M, Zhang Z, Wang X, Li N, Shah N, Zhao Yet al., 2019, Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties, APPLIED ENERGY, Vol: 252, ISSN: 0306-2619

Journal article

d'Amore F, Sunny N, Iruretagoyena D, Bezzo F, Shah Net al., 2019, European supply chains for carbon capture, transport and sequestration, with uncertainties in geological storage capacity: Insights from economic optimisation, Computers and Chemical Engineering, Vol: 129, Pages: 1-18, ISSN: 0098-1354

Carbon capture and storage is widely recognised as a promising technology for decarbonising the energy and industrial sector. An integrated assessment of technological options is required for effective deployment of large-scale infrastructures between the nodes of production and sequestration of CO2. Additionally, design challenges due to uncertainties in the effective storage availability of sequestration basins must be tackled for the optimal planning of long-lived infrastructure. The objective of this study is to quantify the financial risks arising from geological uncertainties in European supply chain networks, whilst also providing a tool for minimising storage risk exposure. For this purpose, a methodological approach utilising mixed integer linear optimisation is developed and subsequent analysis demonstrates that risks arising from geological volumes are negligible compared to the overall network costs (always <1% of total cost) although they may be significant locally. The model shows that a slight increase in transport (+11%) and sequestration (+5%) costs is required to obtain a resilient supply chain, but the overall investment is substantially unchanged (max. +0.2%) with respect to a risk-neutral network. It is shown that risks in storage capacities can be minimised via careful design of the network, through distributing the investment for storage across Europe, and incorporating operational flexibility.

Journal article

González-Garay A, Pozo C, Galán-Martín Á, Brechtelsbauer C, Chachuat B, Chadha D, Hale C, Hellgardt K, Kogelbauer A, Matar OK, McDowell N, Shah N, Guillén-Gosálbez Get al., 2019, Assessing the performance of UK universities in the field of chemical engineering using data envelopment analysis, Education for Chemical Engineers, Vol: 29, Pages: 29-41, ISSN: 1749-7728

University rankings have become an important tool to compare academic institutions within and across countries. Yet, they rely on aggregated scores based on subjective weights which render them sensitive to experts’ preferences and not fully transparent to final users. To overcome this limitation, we apply Data Envelopment Analysis (DEA) to evaluate UK universities in the field of chemical engineering as a case study, using data retrieved from two national rankings. DEA is a non-parametric approach developed for the multi-criteria assessment of entities that avoids the use of subjective weightings and aggregated scores; this is accomplished by calculating an efficiency index, on the basis of which universities can be classified as either ‘efficient’ or ‘inefficient’. Our analysis shows that the Higher Education Institutions (HEI) occupying the highest positions in the chemical engineering rankings might not be the most efficient ones, and vice versa, which highlights the need to complement the use of rankings with other analytical tools. Overall, DEA provides further insight into the assessment of HEIs, allowing institutions to better understand their weaknesses and strengths, while pinpointing sources of inefficiencies where improvement efforts must be directed.

Journal article

Jing R, Kuriyan K, Kong Q, Zhang Z, Shah N, Li N, Zhao Yet al., 2019, Exploring the impact space of different technologies using a portfolio constraint based approach for multi-objective optimization of integrated urban energy systems, Renewable and Sustainable Energy Reviews, Vol: 113, Pages: 1-12, ISSN: 1364-0321

Optimization-based modelling provides valuable guidance for designing integrated urban energy systems. However, modelers have to make certain assumptions and they may lack awareness of realistic conditions such as decision-makers’ preferences on certain technology, which can easily lead the obtained optimal solution to be invalid. Therefore, instead of focusing on one “fragile” optimal solution, this paper provides a systematic overview of the contribution each technology can bring to the whole system design so as to achieve the optimum. To achieve this, a portfolio constraint based approach is proposed, which is inspired by the modelling to generate alternatives (MGA) method as well as the eps-constraint method for multi-objective optimization. By varying the threshold values of portfolio constraints, a series of solutions can be gathered as an “impact space” representing the economic and environmental contributions of each technology for the whole system design. A practical Fitting of Ellipses method is further applied to quantify the size of the impact space. Through observing the formation of the impact space, more valuable insights on system design can be obtained. The proposed approach is applied to a case study of an urban district in Shanghai, China, where a generalized urban energy system model involving commonly used energy supply technologies is established. Various technologies and design options lead to significantly different impact spaces, where CHP is found to have the largest impact on system design. Overall, instead of merely providing decision-maker a very specific solution, this paper introduces a new approach to evaluate multiple technologies when designing integrated urban energy systems.

Journal article

Zhu Y, Shah N, Carré G, Lemaire S, Gatignol E, Piccione PMet al., 2019, Continent-wide planning of seed production: mathematical model and industrial application, Optimization and Engineering, Vol: 20, Pages: 881-906, ISSN: 1389-4420

The seed supply chain is one of most sophisticated elements of the agricultural value chain with long lead times, fragmented structure and high levels of uncertainty. Since the seed industry has received less attention in research compared with other sectors in the agriculture industry, it has enormous potential for improvement due to the lack of comprehensive mathematical optimization applications, increasing competition within the industry and decreasing spare arable land worldwide. All of the existing optimization applications in the seed supply chain have concerned land allocation at the farm level as well as regional level processing and distribution after harvesting. This research closes the gap between farm level planning and regional level distribution through optimization of seed production planning at a regional level, taking account of a number of complex constraints and practical preferences. Compared to a “business as usual” approach, the proposed application can save up to 16% of the total cost as well as 9% land usage and effectively mitigate major risks in the planning phase. The method is evaluated using Syngenta’s industrial case studies.

Journal article

Kuriyan K, Shah N, 2019, A combined spatial and technological model for the planning of district energy systems, International Journal of Sustainable Energy Planning and Management, Vol: 21, Pages: 111-131, ISSN: 2246-2929

This paper describes a combined spatial and technological model for planning district energy systems. The model is formulated as a mixed integer linear program (MILP) and selects the optimal mix of technology types, sizes and fuels for local energy generation, combined with energy imports and exports. The model can also be used to select the locations for the energy sources, the distribution route, and optionally, to select the heat loads that will be connected to a district energy system. The optimisation model combines a map-based spatial framework, describing the potential distribution network structure, with a flexible Resource Technology Network (RTN) representation which incorporates multiple heat sources. Results for scenarios based on a test dataset are presented and show the impact of heat prices on the designed network length. The results illustrate the use of Combined Heat and Power (CHP) units to satisfy internal and external power demands, and also demonstrate their use in combination with heat pumps to satisfy emissions targets. A system value metric is introduced to quantify the incremental impact of investments in the heat network in areas of varying heat density. A procedure for screening potential supply locations to reduce computational requirements is proposed.

Journal article

Bahzad H, Shah N, Dowell NM, Boot-Handford M, Soltani SM, Ho M, Fennell PSet al., 2019, Development and techno-economic analyses of a novel hydrogen production process via chemical looping, International Journal of Hydrogen Energy, Vol: 44, Pages: 21251-21263, ISSN: 0360-3199

In this work, a novel hydrogen production process (Integrated Chemical Looping Water Splitting “ICLWS”) has been developed. The modelled process has been optimised via heat integration between the main process units. The effects of the key process variables (i.e. the oxygen carrier-to-fuel ratio, steam flow rate and discharged gas temperature) on the behaviour of the reducer and oxidiser reactors were investigated. The thermal and exergy efficiencies of the process were studied and compared against a conventional steam-methane reforming (SMR) process. Finally, the economic feasibility of the process was evaluated based on the corresponding CAPEX, OPEX and first-year plant cost per kg of the hydrogen produced. The thermal efficiency of the ICLWS process was improved by 31.1% compared to the baseline (Chemical Looping Water Splitting without heat integration) process. The hydrogen efficiency and the effective efficiencies were also higher by 11.7% and 11.9%, respectively compared to the SMR process. The sensitivity analysis showed that the oxygen carrier–to-methane and -steam ratios enhanced the discharged gas and solid conversions from both the reducer and oxidiser. Unlike for the oxidiser, the temperature of the discharged gas and solids from the reducer had an impact on the gas and solid conversion. The economic evaluation of the process indicated hydrogen production costs of $1.41 and $1.62 per kilogram of hydrogen produced for Fe-based oxygen carriers supported by ZrO2 and MgAl2O4, respectively - 14% and 1.2% lower for the SMR process H2 production costs respectively.

Journal article

O'Dwyer E, Pan I, Acha Izquierdo S, Gibbons S, Shah Net al., 2019, Modelling and evaluation of multi-vector energy networks in smart cities, International Conference on Smart Infrastructure and Construction 2019, Publisher: ICE Publishing

Energy demand growth and the rapid rate of technological changein an urban contextare already having an impact on our energy systems. Considering global ambitions to reduce carbon emissions and minimise the rate and impacts of climate change, this demand will need to be met with energy from low carbon sources. Increased electrification of heat and transport networks is anticipated, however, the cross-sectoral impacts of different interventions in these systems must be better understood to prevent gains in one system leadingto losses in another while ensuring financial benefits for producers and consumers. As such, evaluating the impacts of specific interventions can be a challenge, with analyses typically focussed on individual systems. In this paper, asimulation environment is developed to capture the behaviour of interconnected heat, power and transport networks in an urban environment to act as a ‘digital twin’ for the energy systems of a district or city. The modelling environment illustrated here is based on the smart city interventions in Greenwich (London), with model validation carried out using real data measurements. Building retrofit and heat electrification interventions are demonstrated in terms of costs, energy consumption and CO2 emissions, considering constraints on power and thermal systems.

Conference paper

Acha Izquierdo S, Le Brun N, Shah N, Bird Met al., Assessing the Modelling Approach and Datasets Required for Fault Detection in Photovoltaic Systems, IEEE Industry Applications Society Annual Meeting

Conference paper

Sharifzadeh M, Sikinioti-Lock A, Shah N, 2019, Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression, Renewable and Sustainable Energy Reviews, Vol: 108, Pages: 513-538, ISSN: 1364-0321

Renewable energy from wind and solar resources can contribute significantly to the decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless integration with the grid poses significant challenges due to their intermittent generation patterns, which is intensified by the existing uncertainties and fluctuations from the demand side. A resolution is increasing energy storage and standby power generation which results in economic losses. Alternatively, enhancing the predictability of wind and solar energy as well as demand enables replacing such expensive hardware with advanced control and optimization systems. The present research contribution establishes consistent sets of data and develops data-driven models through machine-learning techniques. The aim is to quantify the uncertainties in the electricity grid and examine the predictability of their behaviour. The predictive methods that were selected included conventional artificial neural networks (ANN), support vector regression (SVR) and Gaussian process regression (GPR). For each method, a sensitivity analysis was conducted with the aim of tuning its parameters as optimally as possible. The next step was to train and validate each method with various datasets (wind, solar, demand). Finally, a predictability analysis was performed in order to ascertain how the models would respond when the prediction time horizon increases. All models were found capable of predicting wind and solar power, but only the neural networks were successful for the electricity demand. Considering the dynamics of the electricity grid, it was observed that the prediction process for renewable wind and solar power generation, and electricity demand was fast and accurate enough to effectively replace the alternative electricity storage and standby capacity.

Journal article

Jing R, Wang M, Zhang Z, Liu J, Liang H, Meng C, Shah N, Li N, Zhao Yet al., 2019, Comparative study of posteriori decision-making methods when designing building integrated energy systems with multi-objectives, Energy and Buildings, Vol: 194, Pages: 123-139, ISSN: 0378-7788

By multi-objective optimization of designing integrated energy systems for buildings, the Pareto frontier can be obtained consisting of a series of optimal compromise solutions. Since all solutions on Pareto frontiers are non-dominated, it is challenging to identify one “best of the best” solution, which requires posteriori multi-criteria decision-making. However, most existing research only presented the obtained Pareto frontiers, while neglected the decision-making. Therefore, this paper compares four posteriori decision-making approaches in recent publications by solving one identical problem to emphasize the importance of decision-making. An illustrative Pareto frontier is generated by two multi-objective optimization approaches, i.e., eps (ɛ)-constraint and Non-dominated Sorting Genetic Algorithm (NSGA-II). Four categories of multi-criteria decision-making methods, i.e., Shannon entropy, Eulerian distance, fuzzy membership function and evidential reasoning, are further implemented. The decision-making results are different when various approaches are applied. The underlying reasons are analyzed including two key factors, i.e. selection of objectives and shape of Pareto frontier, which provides suggestions of using decision-making approaches in future multi-objective optimization research on building energy systems.

Journal article

Hankin A, Guillen Gosalbez G, Kelsall G, Mac Dowell N, Shah N, Weider S, Brophy Ket al., 2019, Assessing the economic and environmental value of carbon capture and utilisation in the UK, Briefing paper, 3

• As a signatory to the 2015 Paris Climate Change Agreement, the UK has committed to an ambitious transformation of its economy.• Decarbonisation of the UK’s economy must be a priority, but carbon-based fuels and platform chemicals will remain important to the global economy; their production from captured carbon dioxide and renewable energy can support this industrial need.• In this Briefing Paper, we report on results of a systematic procedure developed to assess the viability of different carbon capture and utilisation (CCU) pathways.• Our findings on three CCU pathways show that proposed CCU projects should always be assessed on a case-by-case basis, using detailed, UK centric, cradle-to-grave life cycle analyses.• CCU cannot provide the emission mitigation rate of carbon capture and storage (CCS), but as the UK’s entire geological storage capacity is offshore, CCU could mitigate emissions from inland point sources.• Of the considered CCU pathways, presently the production of polyurethane is the most promising for the UK and could provide an immediate short-term mitigation solution for greenhouse gas (GHG) emissions. Currently, methanol production does not appear to be a viable solution.

Report

Kis Z, Shattock R, Shah N, Kontoravdi Cet al., 2019, Correction: Emerging technologies for low‐cost, rapid vaccine manufacture, Biotechnology Journal, Vol: 14, Pages: 1-2, ISSN: 1860-6768

Journal article

Hankin A, Guillen Gosalbez G, Kelsall G, Mac Dowell N, Shah N, Weider S, Brophy Ket al., 2019, Assessing the economic and environmental value of carbon capture and utilisation in the UK, Briefing Note – summary of Briefing Paper No 3

• As a signatory to the 2015 Paris Climate Change Agreement, the UK has committed to an ambitious transformation of its economy.• Decarbonisation of the UK’s economy must be a priority, but carbon-based fuels and platform chemicals will remain important to the global economy; their production from captured carbon dioxide and renewable energy can support this industrial need.• In this Briefing Paper, we report on results of a systematic procedure developed to assess the viability of different carbon capture and utilisation (CCU) pathways.• Our findings on three CCU pathways show that proposed CCU projects should always be assessed on a case-by-case basis, using detailed, UK centric, cradle-to-grave life cycle analyses.• CCU cannot provide the emission mitigation rate of carbon capture and storage (CCS), but as the UK’s entire geological storage capacity is offshore, CCU could mitigate emissions from inland point sources.• Of the considered CCU pathways, presently the production of polyurethane is the most promising for the UK and could provide an immediate short-term mitigation solution for greenhouse gas (GHG) emissions. Currently, methanol production does not appear to be a viable solution.

Report

Howard B, Acha Izquierdo S, Shah N, Polak Jet al., 2019, Implicit sensing of building occupancy count with information and communication technology data sets building and environment, Building and Environment, Vol: 157, Pages: 297-308, ISSN: 0360-1323

Occupancy count, i.e., the number of people in a space or building, is becoming an increasingly important measurement to model, predict, and minimize operational energy consumption. Explicit, hardware-based, occupancy counters have been proposed but wide scale adoption is limited due to the cost and invasiveness of system implementation. As an alternative approach, researchers propose using data from existing information and communication technology (ICT) systems to infer occupancy counts.In the reported work, three different data streams, security access data, wireless connectivity data, and computer activity data, from ICT systems in a medium sized office building were collected and compared to the counts of a commercially available occupancy counter over 59 working days. The occupancy counts from the ICT systems are compared to the commercial counter with and without calibration to determine the ability of the data sets to measure occupancy. Various transformations were explored as calibration techniques for the ICT data sets. Training sets of 24, 48, and 120 hours were employed to determine how long an external calibration system would need to be installed.The analysis found that calibration is required to provide accurate counts. While each ICT data set provides similar magnitudes and time series behavior, incorporating all three data streams in a two layer neural network with 1 week of training data provides the most accurate estimates against 5 performance metrics. Whilst 1 week of data provides the best results, 24 hours is sufficient to develop similar levels of performance.

Journal article

Kotidis P, Demis P, Goey C, Correa E, McIntosh C, Trepekli S, Shah N, Klymenko O, Kontoravdi Ket al., 2019, Constrained global sensitivity analysis for bioprocess design space identification, Computers and Chemical Engineering, Vol: 125, Pages: 558-568, ISSN: 1873-4375

The manufacture of protein-based therapeutics presents unique challenges due to limited control over the biotic phase. This typically gives rise to a wide range of protein structures of varying safety and in vivo efficacy. Herein we propose a computational methodology, enabled by the application of constrained Global Sensitivity Analysis, for efficiently exploring the operatingrange of process inputs in silico and identifying a design space that meets output constraints. The methodology was applied to an antibody-producing Chinese hamster ovary (CHO) cell culture system: we explored >8000 feeding strategies to identify a subset of manufacturing conditions that meet constraints on antibody titre and glycan distribution as an attribute of product quality. Our computational findings were then verified experimentally, confirming the applicability of this approach to a challenging production system. We envisage that this methodology can significantly expedite bioprocess development and increase operational flexibility.

Journal article

Papathanasiou MM, Burnak B, Katz J, Shah N, Pistikopoulos ENet al., 2019, Assisting continuous biomanufacturing through advanced control in downstream purification, Computers and Chemical Engineering, Vol: 125, Pages: 232-248, ISSN: 0098-1354

Aiming to significantly improve their processes and secure market share, monoclonal antibody (mAb) manufacturers seek innovative solutions that will yield improved production profiles. In that space, continuous manufacturing has been gaining increasing interest, promising more stable processes with lower operating costs. However, challenges in the operation and control of such processes arise mainly from the lack of appropriate process analytics tools that will provide the required measurements to guarantee product quality. Here we demonstrate a Process Systems Engineering approach for the design a novel control scheme for a semi-continuous purification process. The controllers are designed employing multi-parametric Model Predictive Control (mp-MPC) strategies and the successfully manage to: (a) follow the system periodicity, (b) respond to measured disturbances and (c) result in satisfactory yield and product purity. The proposed strategy is also compared to experimentally optimized profiles, yielding a satisfactory agreement.

Journal article

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