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

510 results found

Cooper N, Panteli A, Shah N, 2019, Linear estimators of biomass yield maps for improved biomass supply chain optimisation, Applied Energy, 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

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, ISSN: 0306-2619

© 2019 Elsevier Ltd The optimal design of urban energy system is considered as a global challenge for improving urban sustainability, efficiency and resilience. The optimization problem is normally formulated as a mixed-integer programming model. With certain spatial and temporal resolution, the model complexity will increase rapidly when the modelling scale expands. The uncertainty of demand further makes the problem more complex. Therefore, to model large-scale urban energy systems, the trade-off between modelling resolution and computational cost has to be considered. This study introduces a hierarchical based approach to decompose the district-level problem into neighborhood-level sub-problems by clustering technique. Two technical routes are further proposed, (1) the energy hub mode adopts Graph theory techniques to obtain an optimal solution rapidly with a slight sacrifice on optimality; (2) the distributed mode enables high optimality but requires significantly high computational cost. Both two routes deal with multiple uncertainties of cooling and heating demand via stochastic programming. The proposed approach is demonstrated via a case study of a business district in Shanghai. The results indicate that modelling with demand uncertainties can lead to 15% difference on project cost from the deterministic formulation. Demand complementarity and network design turn out have critical impacts on system design and project economics. Moreover, a novel Coefficient of Variation index is proposed quantifying the demand complementarity. In general, the proposed approach is efficient and in line with the procedure of real-world infrastructure development. By such approach, the problem becomes solvable by using ordinary computers, which makes it more applicable in real-world urban developments.

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

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

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

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, 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

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

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

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

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

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

Durkin A, Taptygin I, Kong Q, Mukhtar Gunam Resul MF, Rehman A, Lopez Fernandez AM, Harvey A, Shah N, Guo Met al., 2019, Scale-up and sustainability evaluation of biopolymer production from citrus waste offering carbon capture and utilisation pathway, ChemistryOpen, Vol: 8, Pages: 668-688, ISSN: 2191-1363

Poly(limonene carbonate) (PLC) has been highlighted as an attractive substitute to petroleum derived plastics, due to its utilisation of CO2 and bio-based limonene as feedstocks, offering an effective carbon capture and utilisation pathway. Our study investigates the techno-economic viability and environmental sustainability of a novel process to produce PLC from citrus waste derived limonene, coupled with an anaerobic digestion process to enable energy cogeneration and waste recovery maximisation. Computational process design was integrated with a life cycle assessment to identify the sustainability improvement opportunities. PLC production was found to be economically viable, assuming sufficient citrus waste is supplied to the process, and environmentally preferable to polystyrene (PS) in various impact categories including climate change. However, it exhibited greater environmental burdens than PS across other impact categories, although the environmental performance could be improved with a waste recovery system, at the cost of a process design shift towards energy generation. Finally, our study quantified the potential contribution of PLC to mitigating the escape of atmospheric CO2 concentration from the planetary boundary. We emphasise the importance of a holistic approach to process design and highlight the potential impacts of biopolymers, which is instrumental in solving environmental problems facing the plastic industry and building a sustainable circular economy.

Journal article

Chakrabarti A, Proeglhoef R, Bustos-Turu G, Lambert R, Mariaud A, Acha S, Markides CN, Shah Net al., 2019, Optimisation and analysis of system integration between electric vehicles and UK decentralised energy schemes, Energy, Vol: 176, Pages: 805-815, ISSN: 0360-5442

Although district heat network schemes provide a pragmatic solution for reducing the environmental impact of urban energy systems, there are additional benefits that could arise from servicing electric vehicles. Using the electricity generated on-site to power electric vehicles can make district heating networks more economically feasible, while also increasing environmental benefits. This paper explores the potential integration of electric vehicle charging into large-scale district heating networks with the aim of increasing the value of the generated electricity and thereby improving the financial feasibility of such systems. A modelling approach is presented composed of a diverse range of distributed technologies that considers residential and commercial electric vehicle charging demands via agent-based modelling. An existing district heating network system in London was taken as a case study. The energy system was modelled as a mixed integer linear program and optimised for either profit maximisation or carbon dioxide emissions minimisation. Commercial electric vehicles provided the best alternative to increase revenue streams by about 11% against the current system configuration with emissions effectively unchanged. The research indicates that district heating network systems need to carefully analyse opportunities for transport electrification in order to improve the integration, and sustainability, of urban energy systems.

Journal article

Durkin A, Taptygin I, Kong Q, Gunam Resul MFM, Rehman A, Fernández AML, Harvey AP, Shah N, Guo Met al., 2019, Scale-up and sustainability evaluation of biopolymer production from citrus waste offering carbon capture and utilisation pathway, ChemistryOpen, Vol: 8, Pages: 668-688, ISSN: 2191-1363

Invited for this month's cover picture is the group of Dr Miao Guo from Department of Chemical Engineering at the Imperial College London (UK). The cover picture shows modelling research on the co-polymerisation of waste-sourced limonene oxide with CO2 to produce poly(limonene carbonate), which offers a sustainable pathway to achieve carbon capture and utilisation. A computational approach to process design was integrated with sustainability evaluation to model this synthetic pathway and identify the environmental-damaging and performance-limiting steps for further improvement. Our research highlights the potential of closed-loop manufacturing systems with waste recovery, which is instrumental in building a sustainable circular economy.

Journal article

Wang X, van Dam KH, Triantafyllidis C, Koppelaar RHEM, Shah Net al., 2019, Energy-water nexus design and operation towards the sustainable development goals, COMPUTERS & CHEMICAL ENGINEERING, Vol: 124, Pages: 162-171, ISSN: 0098-1354

Journal article

Rivotti P, Karatayev M, Mourao ZS, Shah N, Clarke ML, Konadu DDet al., 2019, Impact of future energy policy on water resources in Kazakhstan, Energy Strategy Reviews, Vol: 24, Pages: 261-267, ISSN: 2211-467X

As part of its strategic economic and social plan, Kazakhstan has a target of increasing the share of renewables and alternative energy sources in power generation to 50% by 2050. This greatly contrasts with the current situation, where around 90% of electricity is produced from fossil fuels. To achieve the target, the introduction of between 600 and 2000 MW of nuclear power is expected by 2030. This would impact water resources, already under stress due to significant losses, heavy reliance on irrigation for agriculture, unevenly distributed surface water, variations in transboundary inflows, amongst others. This study presents an integrated analysis of the water-energy systems in Kazakhstan, to investigate the water resource availability to support such energy system transition.

Journal article

Olympios A, Le Brun N, Acha Izquierdo S, Lambert R, Shah N, Markides Cet al., Installation of a dynamic controller for the optimal operation of a CHP engine in a supermarket under uncertainty, 32nd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems

This work is concerned with the integration and coordination of decentralized combined heat and power (CHP) systems in commercial buildings. Although extensive research has been performed on theoretically optimizing the design, sizing and operation of CHP systems, less effort has been devoted to an understanding of the practical challenges and the effects of uncertainty in implementing advanced algorithms to real-world applications. This paper provides details of an undergoing field trial involving the installation of a dynamic controller for the optimal operation of an existing CHP engine, which provides electricity and heat to a supermarket. The challenges in developing and applying an optimization framework and the software architecture required to implement it are discussed. Deterministic approaches that involve no measure of uncertainty provide limited useful insight to decision makers. For this reason, the methodology here develops a stochastic programming technique, which performs Monte Carlo simulations that can consider the uncertainty related to the exporting electricity price. The method involves the formation of a bi-objective function that represents a compromise between maximizing the expected savings and minimizing the associated risk. The results reveal a risk-return trade-off, demonstrating that conservative operation choices emerging from the stochastic approach can reduce risk by about 15% at the expense of a noticeably smaller reduction of about 10% in expected savings.

Conference paper

O'Dwyer E, Pan I, Acha Izquierdo S, Gibbons S, Shah Net al., 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

Ayoub AN, Gaigneux A, Le Brun N, Acha S, Lambert R, Shah Net al., 2019, The development of a carbon roadmap investment strategy for carbon intensive food retail industries, International Conference on Sustainable Energy and Resource Use in Food Chains including Workshop on Energy Recovery Conversion and Management, Publisher: Elsevier, Pages: 333-342, ISSN: 1876-6102

This work presents an approach to develop an innovative decarbonisation investment strategy framework for carbon intensive UK industries by using statistical analysis and optimisation modelling. The case study focuses on taking a representative sample of retail buildings and assesses the financial viability of installing low-carbon Combined Heat and Power units (CHPs) and Photovoltaic Solar Panels (PVs) across a portfolio of buildings. Simulation of each building are initially conducted, and the results generate a set of regression coefficients, via a multivariate adaptive regression splines (MARS), which are inputted into a Mixed Integer Linear Programming (MILP) problem. Solving the MILP yields the optimal decarbonisation investment strategy for the case study up to 2050, considering market trends such as electricity prices, gas prices and policy incentives. Results indicate the level of investment required per year, the operational and carbon savings associated, and a program for such investments. This method is reiterated for several scenarios where different parameters such as utility prices, capital costs and grid carbon factors are forecasted up to 2050 (following the Future Energy Scenarios from National Grid). This work shows how a clear mathematical framework can assist decision-makers in commercial organisations to reduce their carbon footprint cost-effectively and thus reach science-based targets.

Conference paper

Thaore V, Moore S, Polizzi K, Freemont P, Shah N, Kontoravdi Cet al., Cell-free multi-enzyme system for the industrial production of fine chemicals, Chemical Engineering Day UK 2019

Conference paper

Efstratiadi M, Acha Izquierdo S, Shah N, Markides Cet al., 2019, Analysis of a closed-loop water-cooled refrigeration system in the food retail industry: A UK case study, Energy, ISSN: 0360-5442

Refrigeration in supermarkets accounts between 30% and 60% of total electricity demand in UK stores. The aim of this study is to conduct a pre-feasibility analysis of whether the use of a water-cooled configuration rejecting heat to the soil can improve the overall cooling performance of commercial refrigeration systems against air-cooled designs. In this work, a model simulating the operation of an existing refrigeration system is presented and validated against field data measurements taken from a supermarket. The examined system is used as a baseline and then modified to evaluate the impact of installing a water-cooled gas cooler. Results indicate that the use of water-cooled gas coolers has the potential to reduce electrical consumption of refrigeration systems by up to a factor of 5 when external temperatures are high. Overall, annual operation indicates the water-cooled alternative uses 3% less electricity than the air-cooled approach. A hybrid system is also considered consisting of coupled air-cooled and water-cooled units operating in parallel, for which an energy reduction of 6% is obtained compared against the baseline system. An economic evaluation of these systems shows promising results with a payback period of about 5 years for systems installed in new stores, although retrofits are costlier.

Journal article

O'Dwyer E, Pan I, Acha S, Shah Net al., 2019, Smart energy systems for sustainable smart cities: Current developments, trends and future directions, Applied Energy, Vol: 237, Pages: 581-597, ISSN: 0306-2619

Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

Journal article

Sharifzadeh M, Sadeqzadeh M, Nejadghaffar Borhani T, Guo M, Murthy Konda NVSN, Cortada Garcia M, Wang L, Hallett J, Shah Net al., 2019, The multiscale challenges of biomass fast pyrolysis and bio-oil upgrading: review of the state of art and future research directions, Progress in Energy and Combustion Science, Vol: 71, Pages: 1-80, ISSN: 1873-216X

Biomass fast pyrolysis is potentially one of the cheapest routes toward renewable liquid fuels. Its commercialization, however, poses a multi-scale challenge, which starts with the characterization of feedstock, products and reaction intermediates at molecular scales, and continues with understanding the complex reaction network taking place in different reactor configurations, and in the case of catalytic pyrolysis and upgrading on different catalysts. In addition, crude pyrolysis oil is not immediately usable in the current energy infrastructure, due to undesirable properties such as low energy content and corrosiveness as a result of its high oxygenate content. It, therefore, needs to be upgraded and fractionated to desired specifications. While various types of pyrolysis reactors and upgrading technologies are under development, knowledge transfer and closing the gap between theory and application requires model development. In-depth understanding of the reaction mechanisms and kinetics should be combined with the knowledge of multi-scale transport phenomena to enable design, optimization, and control of complex pyrolysis reactors. Finally, underpinning economic and environmental impacts of biofuel production requires expanding the system boundaries to include the overall process and supply chain. The present contribution aims at providing a comprehensive multi-scale review that discusses the state of the art of each of these aspects, as well as their multi-scale interactions. The study is mainly focused on fast pyrolysis, although reference to other types of pyrolysis technologies is made for the sake of comparison and knowledge transfer.

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, 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

Sharifzadeh M, Hien RKT, Shah N, 2019, China's roadmap to low-carbon electricity and water: Disentangling greenhouse gas (GHG) emissions from electricity-water nexus via renewable wind and solar power generation, and carbon capture and storage, Applied Energy, Vol: 235, Pages: 31-42, ISSN: 0306-2619

Electricity and water form an intricate nexus, in that water is crucial for power generation, and electricity (or other primary forms of energy) is the key enabler for water purification and waste-water treatment. Nonetheless, both energy conversion and water purification result in substantial amounts of greenhouse gas (GHG) emissions. These negative interactions with potential “snowball” effect, can be decoupled via the deployment of renewable power generation, and carbon capture from fossil-fuelled technologies. However, such retrofits pose new challenges as wind and solar energy exhibit intermittent generation patterns. In addition, integrating thermal power plants with carbon capture and storage (CCS) imposes energy penalties and increases water requirements. In the present research, an optimization framework is developed which enables systematic decision-making for the retrofit of existing power and water infrastructure as well as investment in renewable and green technologies. A key aspect of the applied framework is the simultaneous optimization of design and operational decisions in the presence of uncertainties in the water demand, electricity demand, as well as wind and solar power availability. The proposed methodology is demonstrated for the case of the water-electricity nexus in China, and provides in-depth insights into regional characteristics of low carbon electricity generation, and their implications for water purification and wastewater treatment, demonstrating a roadmap towards sustainable energy and electricity.

Journal article

Mazur C, Hoegerle Y, Brucoli M, Van Dam K, Guo M, Markides C, Shah Net al., 2019, A holistic resilience framework development for rural power systems in emerging economies, Applied Energy, Vol: 235, Pages: 219-232, ISSN: 0306-2619

Infrastructure and services within urban areas of developed countries have established reliable definitions of resilience and its dependence on various factors as an important pathway for achieving sustainability in these energy systems. However, the assessment, design, building and maintenance of power systems situated in rural areas in emerging economies present further difficulties because there is no a clear framework for such circumstances. Aiming to address this issue, this paper combines different visions of energy-related resilience both in general and under rural conditions in order to provide a robust practical framework for local and international stakeholders to derive the right actions in the rural context of emerging economies. An in-depth review is implemented to recompile information of resilience in general, in energy systems and in rural areas in particular, and a number of existing frameworks is also consulted. In order to acknowledge the particular circumstances and identify the important factors influencing the resilience of rural electrification in emerging economies, a holistic rural power system resilience framework is developed and presented. This consists of twenty-one indicators for technical resilience, eight indicators for social resilience, and thirteen indicators for economic resilience. This framework can be used by system owners and operators, policy makers, NGOs and communities to ensure the longevity of power systems. This work also paves the way for the creation of appropriate and effective resilience standards specifically targeted for application in these regions - aiming to achieve the delivery of global and local sustainability goals.

Journal article

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