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Uncertain programming model for designing multi-objective reverse logistics networks
Get PDFGiven the contradiction between the rapid growth of products and the modest recovery rate of end-of-life products, there is a pressing need to understand the societal significance of establishing a reverse logistics network for end-of-life products. This research constructs an open-loop five-tier reverse logistic network model encompassing customers, centres for collection, disassembly and inspection, remanufacturing, and disposal. A multi-objective mixed-integer nonlinear programming model under uncertainty has been developed. Unlike previous research, this model accounts for surrounding residents' disutility of facilities while simultaneously minimizing economic costs and environmental impact. Besides, uncertainty theory is introduced in solving the proposed model. More specifically, the formulated model converts all uncertain variables into uncertain distributions by implementing the uncertain multi-objective programming method. Furthermore, a customised non-dominated sorting genetic algorithm III (NSGA-III) is proposed and is employed for the first time to address facility selection and recycling volume distribution within the network. The model is then validated using a real-life case study focusing on end-of-life vehicles in Changchun, China. This research could assist decision-makers in both governmental and private sectors in achieving a balanced approach to the interests of the economy, environment, and local communities comprehensively when designing reverse supply chains.Cleaner Logistics and Supply Chai
Research and innovation identified to decarbonise the maritime sector
Get PDFThe maritime sector requires technically, environmentally, socially, and economically informed pathways to decarbonise and eliminate all emissions harmful to the environment and health. This is extremely challenging and complex, and a wide range of technologies and solutions are currently being explored. However, it is important to assess the state-of-the-art and identify further research and innovation required to accelerate decarbonisation. The UK National Clean Maritime Research Hub have identified key priority areas to drive this process, with particular focus on marine fuels, power and propulsion, vessel efficiency, port operations and infrastructure, digitalisation, finance, regulation, and policy.This article was delivered by the UK National Clean Maritime Research Hub established on the 1st September 2023 supported by the UK Department for Transport (DfT) as part of the UK Shipping Office for Reducing Emissions (UK SHORE) Programme and Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/Y024605/1]
Environmental Temperature and Material Characterisation of Planar Microwave Evanescent Sensors for Environmental Analysis
Get PDFPoster contribution to the Defence and Security Doctoral Symposium 2023
A unit product energy mapping framework for operation management in manufacturing industries
Get PDF31st CIRP Conference on Life Cycle Engineering (LCE 2024), 19-21 June 2024, Turin, ItalySustainability has emerged as a primary concern across a wide range of industries, particularly in manufacturing due to its energy-intensive nature. To understand the environmental impact of manufacturing processes and make them less detrimental to the environment users monitor and track energy consumption data. Although this approach is valuable in assessing the overall impact, energy consumption mapping needs to be conducted per product to compare and assess different process strategies. Available research in literature, provides unit process energy consumption models in isolation from manufacturing operations, neglect of machine and operational variations, and limited consideration of detailed data acquisition for indirect energy consumption. This paper presents a comprehensive framework designed to address the existing gaps in the literature on energy consumption mapping within the manufacturing industry. The proposed framework provides a solution by offering a structured approach to data collection, analysis, and utilization within manufacturing processes, aiming to achieve two main outcomes: the calculation of embodied energy per unit product and the provision of systematically analysed data for operation management to enhance energy efficiency. Four key steps constitute the framework: data acquisition, simulation and modelling, impact assessment, and operation management. The data acquisition step involves the identification of manufacturing process flows, equipment specifics, and process parameters, emphasizing machine operation requirements and power readings. These elements are systematically logged into a database providing essential information for both embodied energy calculation and simulation purposes. Results obtained from simulations are subjected to analysis in the impact assessment step to assess embodied carbon and overall environmental impacts. The collective findings from the first three steps are then utilized for operation management.This work was performed as part of the Metallic Aerospace Structures Technologies for Eco-social Returns (MASTER) project, which has received funding from the UKRI ATI program under grant agreement 103040.31st CIRP Conference on Life Cycle Engineering (LCE 2024)Procedia CIR
Machine learning screening tools for the prediction of extraction yields of pharmaceutical compounds from wastewaters
Get PDFPharmaceutical compounds have become an increasingly important source of pollutants in wastewaters being conventional treatments ineffective in removing them, so they are commonly discharged into the environment. Pharmaceuticals can be successfully removed using liquid-liquid extraction, and COSMO-RS can be used to predict interactions and identify the most promising solvents. However, COSMOtherm models cannot account for key process parameters, which reduces the accuracy of these computational models. Therefore, there is a need for alternative computational approaches to accurately predict the extraction yields of pharmaceuticals which can incorporate both processing and interaction variables. This work used machine learning to predict the extraction yield of eleven pharmaceuticals using eight solvents. Six regression models and two classification models were explored. The best performance was obtained with ANN regressor (test MAE: 4.510, test R2: 0.884) and RF classifier (test accuracy: 0.938, test recall: 0.974). The RF regression analysis and classification also showed key extraction yield features: solvent-to-feed ratio, n–octanol–water partition coefficient, hydrogen bond and Van der Waals contributions to excess enthalpy, and pH distance to nearest pKa. Machine learning showed as an excellent tool for screening and selecting the most promising solvents and process conditions to remove pharmaceuticals from wastewater.This work was supported by Comunidad Autónoma de Madrid [project numbers P2018/EMT-4341 and PR65/19-22441]. Diego Rodríguez-Llorente thanks Ministerio de Ciencia, Innovación y Universidades for awarding an FPU grant (FPU18/01536)
Particle-filter-based fault diagnosis for the startup process of an open-cycle liquid-propellant rocket engine
Get PDFThis study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-00164702).Sensor
A template for creating and sharing ground truth data in digital forensics
Get PDFGround truth data (GTD) is used by those in the field of digital forensics (DF) for a variety of purposes including to evaluate the functionality of undocumented, new, or emerging technology and services and the digital traces left behind following their usage. Most accepted and reliable trace interpretations must be derived from an examination of relevant GTD, yet despite the importance of it to the DF community, there is little formal guidance available for supporting those who create it, to do so in a way that ensures any data is of good quality, reliable, and therefore usable. In an attempt to address this issue, this work proposes a minimum standard of documentation that must accompany the production of any GTD, particularly when it is intended for use in the process of discovering new knowledge, proposing original interpretations of a digital trace, or determining the functionality of any technology or service. A template structure is discussed and provided in Appendix S1 which sets out a minimum standard for metadata describing any GTD's production process and content. It is suggested that such an approach can support the maintenance of trust in any GTD and improve the shareability of it.Journal of Forensic Science
Temperature-Bias Compensation of Low-Cost Inertial Sensors – Possible or Pipe Dream?
Get PDFNavigation using low-cost inertial sensors costing less than £1 each is generally considered impossible. With various measurement error contributions, the velocity and position estimates from these sensors drift exponentially with time. By simulating the sensor, we show how the zero bias error is the most serious contributor. The zero bias is known to change with temperature due to dissimilar thermomechanical characteristics of materials in the sensor’s construction and others have shown this trend to be nonlinear, exhibit hysteresis and unique to each sensor. This is a problem because it suggests error compensation by modelling (software level), or sensor redundancy (hardware level) will be ineffective. From temperature experiments on three of the same low-cost sensors, we show that temperature-bias responses are indeed unique and nonlinear but may be opposing between sensors. Furthermore, we show that one can get lucky and obtain a sensor with an axis that is relatively insensitive to temperature. This is encouraging because it supports the idea that an inertial measurement unit comprised of an array of inertial sensors can be fused to provide higher accuracy measurements than a single sensor operating alone. Lastly, we identify a threat to this idea we call temperature shock and suggest how it can be avoided. While the contributions of this work are intended to improve the accuracy of human position tracking, their impact extends to any field where lengthy periods of position tracking under Global Positioning System (GPS) denial is required.Industrial CASE Account - Cranfield University 201
Navigating the blurred work-life boundary under the hybrid working context: how the appraisal of emotions prompts individual boundary management tactics
No full textHybrid working heightens work-life boundaries blurring. This can create the need for employees to constantly navigate evolving demands in their work-nonwork environment, often before formalized strategic, organisational, and managerial interventions are developed and/or implemented. Therefore, understanding why and how adapt to boundary blurs and manage their work-life balance is critical for effective hybrid working. Drawn on appraisal theories of emotion, we argue that employees' appraisals of boundary-blurring situations can elicit emotions, which prompt employees to navigate and adapt to their blurred boundaries in their work-life balance. We substantiate this with a longitudinal one-month dairy and post-interview study using a sample of 34 employees in the UK Higher Education (HE) sector, where hybrid working has been widely applied. Our finding unveils that both positive and negative emotions help the individual efficiently respond to boundary blurring, using prevention- and promotion-oriented tactics; the former associated with negative emotions focuses on the temporary work-life blurring, whereas the latter associated with positive emotions that attempts to leverage resources and opportunities for long term work-life balance. The findings make practical implications for employees and organisations on how to effectively manage hybrid working by understanding work-life boundaries
FinTech, financial inclusion, and different dimensions of inequality: channels and evidence
Get PDFThis study investigates the relationship between FinTech, financial inclusion, and different dimensions of inequality, (gender, income, and carbon) for a panel of 113 nations using the Global Findex waves of survey data from 2011, 2014, 2017, and 2021. The study employs structural equation modelling to simulate both the direct and indirect effects. The key findings include (i) FinTech significantly increases income inequality and reduces gender inequality (through financial inclusion). (ii) FinTech promotes financial inclusion. (iii) Financial inclusion reduces all dimensions of inequality. (iv) Regulatory quality, education, and access to credit reduce different dimensions of inequality through financial inclusion. These results add to a modest but growing body of work on the role of FinTech and financial inclusion in fostering better resource distribution and inclusive development across countries. Moreover, this is the first study to investigate different dimensions of inequality and model interrelationships. The study provides preliminary evidence of the varying distributional effects of FinTech and financial inclusion on different dimensions of inequality