The maximisation of renewable energy generation is critical for net-zero aspiring countries around the globe. Local energy markets facilitate the seamless incorporation of energy from distributed renewable energy resources into the electricity network, serving as platforms for trading locally-generated renewable energy between prosumers in residential communities. However, local energy markets' essential role in distributed energy resource integration is not enough to encourage participation. Prosumers are more likely to join a local energy market if financial incentives are offered. To address this, we present the Flexible Aggressiveness Probabilistic Optimisation (FAPO) bidding strategy for trading electricity within a local energy market aimed at maximising participation incentives. This is formulated as an optimisation problem targeting the maximisation of prosumers' individual utilities. The FAPO methodology is applied in a simplified local energy market simulation environment, and its results are compared to two other well-established bidding strategies: Zero Intelligence-Constrained and Adaptive Aggressiveness. The results indicate that FAPO achieved a wider range of clearing prices than both Adaptive Aggressiveness and Zero Intelligence-Constrained, incentivising greater prosumer participation. Specifically, FAPO enabled the trading of 1.48 MWh of electricity, compared to 1.34 MWh with Adaptive Aggressiveness and 1.37 MWh with Zero Intelligence-Constrained. Furthermore, FAPO cleared 100% of all asks and 98% of all bids, while the other two strategies cleared approximately 90% of submitted orders. Consequently, FAPO is proven to be an engaging bidding methodology likely to attract more prosumers to local energy markets. This is critical for the successful acceptance, uptake, and widespread application of this financial market type, which is key for smooth distributed energy resource integration into the network.

Transportation is one of the major sources of greenhouse gas (GHG) in the United Kingdom (UK), which motivates the government’s policies to perform electrification of transportation and stimulate the use of electric vehicle (EV). However, many practical factors including battery costs and charging infrastructure are significantly affecting the wide utilisation of EVs. In this work, a multiple-output Gaussian processes (GPs) based Global Sensitivity Analysis (GSA) method is employed to investigate the relationship between these EV properties and the cost-performance. The variance-based GSA computations provide the detailed understandings of these key vehicle parameters for EV end-users and manufacturers. It is initially found that the total travel distances and its relevant variables available ID and fraction, and battery efficiency are the dominant factors for the cost of performance within all optimal solutions.

Abstract. Assessing the robustness of a water resource system's performance under climate change involves exploring a wide range of streamflow conditions. This is often achieved through rainfall–runoff models, but these are commonly validated under historical conditions with no guarantee that calibrated parameters would still be valid in a different climate. In this note, we introduce a new method for the statistical generation of plausible streamflow futures. It flexibly combines changes in average flows with changes in the frequency and magnitude of high and low flows. It relies on a three-parameter analytical representation of the flow duration curve (FDC) that has been proved to perform well across a range of basins in different climates. We rigorously prove that, for common sets of streamflow statistics mirroring average behaviour, variability, and low flows, the parameterisation of the FDC under this representation is unique. We also show that conditions applied to these statistics for a solution to exist are commonly met in practice. These analytical results imply that streamflow futures can be explored by sampling wide ranges of three key flow statistics and by deriving the corresponding FDC in relation to model basin response across the full spectrum of flow conditions. We illustrate this method by exploring in which hydro-climatic futures a proposed run-of-river hydropower plant in eastern Turkey is financially viable. Results show that, contrary to approaches that modify streamflow statistics using multipliers applied uniformly throughout a time series, our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows. This matches expected impacts of climate change in the region and supports analyses of the financial robustness of the proposed infrastructure to climate change. We conclude by highlighting how refinements to the approach could further support rigorous explorations of hydro-climatic futures without the help of rainfall–runoff models.

Technoeconomic modelling links engineering performance to economic outcomes. In this paper we present a method for embedding technoeconomic unit process models within a geographically distributed supply chain simulator. This enables the simultaneous optimization of how and where processing is performed. This is particularly suited to waste remediation, where the costs of transporting hazardous materials may outweigh those of performing some stages of treatment at the waste source. The approach is applied to a microalgal-bacterial remediation of hydrocarbon waste (sump oil mainly) produced by the MOD Sites (military bases) across the UK. Through this treatment, valuable bioproducts are recovered from algae which consumes hydrocarbon waste and CO2 as it grows.

To date over 80,000 metal-organic framework (MOF) structures have been synthesised and only ca. 3% of these have had their adsorption capabilities measured for storing oxygen alone. As such, in order to aid the process of producing top-performing MOFs for storing various gases, accurate methods to predict the deliverable capacity of MOFs that have their synthesis method already known is increasingly important. For this purpose, this paper develops a reduced order model (ROM) that can predict the deliverable capacity of synthesised MOFs irrespective to the storage gas across similar gases. The ROM is constructed by identifying the active subspaces through a Sobol' index-based global sensitivity analysis (GSA). The resulting Gaussian process (GP) regression model efficiently predicts the deliverable capacity given a MOFs pore properties with this reduced dimensional space. This approach was applied to a practical MOF exploration example by training a ROM with 2745 MOFs storing methane at 30 bar. The ROM was robustly tested and analysed before using it to predict the deliverable capacity of 82,221 synthesised MOFs storing oxygen at 30 bar. To ensure validity in the exploration example, the predictions produced from the methane trained ROM were compared to a separate ROM trained using the same MOFs but storing oxygen gas. The methane trained ROM was found to be in agreement with the oxygen trained ROM, and was shown to be a viable tool to identify the top-performing MOF structures for oxygen storage. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

In this work, we present a variance-based global sensitivity analysis (GSA) using Gaussian process (GP) surrogate models to calculate the semi-analytic Sobol’ indices for a carbon dioxide (CO2), post-combustion capture (PCC) process. Using the open-source ROM-COMMA software library, the generated GP surrogate model enables accurate process output prediction, and calculation of the semi-analytic Sobol’ indices for GSA. We apply this methodology to a case study of a CO2 PCC process with 30 wt.% Monoethanolamine (MEA). Results showed an excellent prediction quality, further identifying key process parameters for outputs variables such as the cost of CO2 capture (US$/tCO2) and the CO2 capture rate (% mol.).These findings have applications in dimensionality reduction, process optimisation, operation and control of PCC processes, as PCC via MEA absorption is at the forefront of amine absorption technologies owing to its performance and maturity. It is also a key part of carbon capture, utilisation and storage (CCUS) which has been identified as a growing and important decarbonisation route across a wide range of industry application and regions. This GP surrogate model presented meets the growing need for computationally efficient models to aid quick and accurate prediction of process conditions, and the identification of key process parameters over a wide range of industrial applications, as opposed to rigorous, application-specific, and computationally expensive simulation models.

This is a first comparison of the sequential design of experiments strategy and global sensitivity analysis for nanomaterials, thus enabling sustainable product and process design in future.

Protein extraction is essential to the design and manufacture of bioproducts. A benign method of increasing academic and commercial interest is aqueous two-phase extraction (ATPE) (Iqbal et al., 2016), wherein a phase forming additive (in this case polyethylene glycol with phosphate buffer) separates an aqueous mixture into a target-rich top phase and a contaminant-rich bottom phase. This paper concerns a multistage separation, performed by repeating the ATPE iteratively to achieve a target protein yield or purity (Rosa et al., 2009). A McCabe-Thiele diagram is used to compute the number of stages required to achieve the target. Unfortunately, the protein phase equilibrium curves used to construct the McCabe-Thiele diagram are subject to substantial variation depending on the precise composition and temperature of the mixture being separated. In order to assess the reliability of a multistage ATPE, we examine the robustness of the McCabe-Thiele diagram to variations in the phase equilibrium curves. It is seen that error propagation is weak, in that the same number of stages are required to achieve target under a variety of error scenarios. When this is not the case, a novel method of predicting the number of extra stages required is presented.

Model-driven design requires a well-calibrated model and therefore needs efficient workflows to achieve this. This efficiency can be achieved with the identification of the critical process parameters (CPPs) and the most impactful modelling parameters followed by a targeted experimental campaign to prioritise the calibration of these. To identify these parameters it is essential to perform a global sensitivity analysis (GSA). Here, an efficient GSA is applied to a wet granulation case study with the Sobol' indices used to identify the CPPs and impactful modelling parameters. The population balance, mechanistic model that is used requires considerable computational effort for a GSA so a Gaussian Process surrogate is utilised to inter-rogate the underlying model. These key results reduce the input-space by 80% enabling the proposal of a targeted experimental design and model calibration workflow. This substantially improves the ability to deploy model-based design to determine the impactful parameter values, reducing the experimental effort by 42:1% compared to a conventional experimental design. (c) 2021 Elsevier Ltd. All rights reserved.

A particular safety issue with Lithium-ion (Li-ion) cells is thermal runaway (TR), which is the exothermic decomposition of cell components creating an uncontrollable temperature rise leading to fires and explosions. The modelling of TR is difficult due to the broad range of cell properties and potential conditions. Understanding the effect that thermo-physical and heat transfer characteristics have on the TR abuse model output is essential to develop more accurate and robust TR models. This study uses global sensitivity analysis (GSA) to investigate the effect of the cell parameters on the outcome of TR events. Using a Gaussian Process (GP) surrogate model to calculate the Sobol’ indices, it is shown that the emissivity value is the dominant thermo-characteristic throughout the overall abuse scenario. Further analysis, investigating three key TR features shows the conductivity coefficient to be the most important with respect to the maximum temperature reached during TR. Results demonstrate that researchers can confidently estimate some thermo-characteristics but require accurate characterisation of the emissivity and conductivity coefficient to ensure robust predictions. Given the importance of battery technology to aid in global de-carbonisation, these findings are key to increasing their safe design and operation.

Electricity retailers and power generators have an increasing potential to profit from selling and purchasing electricity as wholesale electricity prices are encouraged to be introduced to both industrial and domestic customers. Hence, this paper focuses on developing an efficient method to aid decision-makers in forecasting the hourly price of electricity 4 weeks ahead. The method developed in this paper uses an approach to hybridize Gaussian Process (GP) regression with clustering to improve the predictive capabilities in electricity price forecasting. By first clustering all the input data and introducing a cluster number as a new input variable, the GP is conditioned to aid predictive process through data similarity. This proposed method has been successfully applied to real electricity price data from the United Kingdom, comparing the predictive quality of the novel method (GPc1) to that of an original GP and that of a method which pre-clusters and filters the data for numerous GPs (GPc2). By comparing the predictive price distributions to the observed prices in the month of December 2018, it was found that clustering improves the predicted mean values of a GP while the mean predictive quality of GPc1 and GPc2 are of equal standing. Therefore, the number of outliers at 2 STD's were compared, showing GPc1 to have a predicted distribution with uncertainty that covers more of the true electricity prices than that of GPc2. In conclusion, the novel method provides the decision-maker with greater reliability so that the true electricity prices will be within the confidence limits predicted.

Lithium ion (Li-ion) cells are the most prominent electrochemical energy storage device in todays world as they are utilised in many applications across many scales. However, Li-ion cells can suffer from a severe safety issue known as thermal runaway (TR). This process is due to exothermic chemical decomposition of a cells components. Being able to understand and model this process is essential for the development of safer batteries. Lithium iron phosphate (LFP) cells are known to have the safest chemistry of Li-ion cells. However, TR models developed for LFP cells have not been validated and when compared to new experimental work are shown to be inaccurate. Hence, the development of an accurate and validated LFP TR model is the focus of present research. Classical TR modelling of Li-ion cells utilises four Arrhenius equations to predict the reaction rate of these reactions and in turn the heat generated. However, the development of a TR model (via parameter estimation) is difficult due to 1) the large range some of the parameters of the Arrhenius equation can take i.e. several 10s order of magnitude between literature values for a given reaction, 2) the complex interaction between different parameters within an Arrhenius equation, and 3) the effect of the heat generated by each reaction on the others. Given this, direct minimisation of the root mean squared error (RMSE) between simulated and experimental results has proven to be difficult, with optimized parameters unable to represent important experimental features. As such, an approach is developed in which the viable parameter space is defined and sampled using a pseudo-random sampling. The error of the heuristic fit (RMSE) is then emulated by a Gaussian Process, further refined by Global Sensitivity Analysis. The result is an emulator providing predictions (with attached uncertainties a part of the process) as a function of a reduced number of combined parameters. This is a reduction in model order achieved by a novel, optimal rotation of input basis.