20505 research outputs found
Sort by
A three-step weather data approach in solar energy prediction using machine learning
Get PDFSolar energy plays a critical part in lowering CO2 emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model's accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.This research was supported by the Petroleum Technology Development Fund (PTDF) [PTDF/ED/OSS/PHD/TOF/1945/20].Renewable Energy Focu
Effect of biochar addition on biogas production using konjac waste through mesophilic two-phase anaerobic digestion
Get PDFPurpose: This study aimed to investigate the influence of biochar supplementation in the mesophilic two-phase anaerobic digestion process for konjac waste.
Methods: Reactors with a working volume of 60 mL were utilised to incubate cultures containing biochar. In the first phase, the cultures were maintained at 32 °C and a pH of 5 to facilitate the production of hydrogen. In the second phase, the cultures were adjusted to 37 °C and a pH of 7 to promote methane production. The concentration of konjac flour waste varied between 0 and 500 g/L, while the ratio of biochar addition ranged from 0 to 25 g/L. Biogas production was measured daily using the volume displacement method, and the pH of the cultures was monitored both before and after the experiment.
Results: The findings show that using biochar has a beneficial effect on biogas production from konjac flour waste. During the initial phase of the experiment, incorporating biochar with a concentration of 15 g/L led to substantial improvements, elevating the maximum H2 production rates by 9.6%. Furthermore, the addition of biochar led to an 84% increase in H2 yield during the initial phase. Similarly, in the second phase, introducing biochar with a concentration of 15 g/L resulted in a 22.2% increase in the maximum CH4 production rates and a 2.5 times increase in CH4 yield.
Conclusions: Overall, this work confirms the beneficial effects of biochar on the H2 and CH4 production from konjac flour waste using the mesophilic two-phase anaerobic digestion method for sustainable energy.Partial financial support for this research was received from the Institute of Research and Community Services Universitas Brawijaya (LPMM UB) and the Faculty of Agricultural Technology, Universitas Brawijaya.Journal of Biosystems Engineerin
Soil–plant–pollinator relationships in urban grass and meadow habitats: competing benefits and demands of tall flowering plants on soil and pollinator diversity
Get PDFUrban green spaces can be important habitats for soil, plant, and pollinator diversity and the complementary ecosystem functions they confer. Most studies tend to investigate the relationships between plant diversity with either soil or pollinator diversity, but establishing their relationship across habitat types could be important for optimising ecosystem service provision via alternative management (for instance, urban meadows in place of short amenity grass). Here, we investigate soil–plant–pollinator relationships across urban grass and meadow habitats through a range of measured biodiversity (soil mesofauna and macrofauna, plants, aboveground invertebrates, and pollinators) and edaphic variables. We found significant effects of habitat type on available nutrients (plant and soil C:N ratios) but less clear relationships were observed between habitat type and diversity metrics. Soil–plant–pollinator interactions across habitat types and sites showed an interconnection, whereby flowering plant abundance increased alongside soil macrofauna abundance. Site characteristics that showed strong effects on plant and invertebrate diversity metrics were C:N ratios (plant and soil) and soil pH, suggesting a potential role of nutrient availability on soil–plant–pollinator associations. Our results suggest that a combination of short-mown grass, tall grass, and sown flowers can provide greater benefits for soil and pollination services as each habitat type benefits different taxa due to differing sensitivities to management practices. For example, pollinators benefit from sown flowers but soil fauna are sensitive to annual sowing. Our results also indicate that sown flowers may not optimise overall biodiversity as expected due to disturbance and the depleting role of tall, flowering plants on soil nutrient availability. Future research across a greater range of sites in urban landscapes would resolve the potential role of nutrient availability in modulating soil–plant–pollinator interactions in urban green spaces.Natural Environment Research Council (NERC)Diversit
Unlocking digital growth: overcoming barriers to digital transformation for Indian food SMEs
Get PDFThis paper aims to study how firms must be agile to overcome risks and manage cost repercussions. Specifically, it focuses on promoting digitalization in Indian food SMEs for greater competitiveness. The main purpose is to design a model for implementing robust interventions in a rational manner. To achieve this, a mixed approach, including a literature review and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, was chosen. This approach is recommended for addressing barriers to digital transformation in SMEs. The results suggest that the absence of internet connectivity and problems related to organization impede the efficiency of operations in small and medium-sized Indian food businesses. By overcoming these obstacles and allocating resources to enhance their digital capacities, stakeholders can effectively shape their future business operations. In addition, it is imperative for the stakeholders to actively adopt and utilize a range of digital tools such as blockchain, IoT, Big Data, and cloud computing. To implement and sustain digital transformation effectively, three foundational elements are crucial: internet availability, financial resources, and employee training. This research offers an innovative approach to the practiceners and mangers to adopt digital transformation of Indian food SMEs.Discover Foo
Narrative emotions and market crises
Get PDFRobert Shiller highlights the role popular stories play in driving economic behavior and argues the need to analyze these scientifically. However, their impacts are difficult to measure directly and often conflict. We show the strength of such stories resides in the emotions they generate, and that the tenor and persuasiveness of financial narratives and their association with the market can be empirically quantified. Specifically, we textually analyze financial media reports to identify the different powerful investor emotions manifest during three recent extreme market periods, dot.com mania, the Global Financial Crisis and the COVID-19 pandemic, constructing original context-specific emotion word dictionaries for this purpose. We find investor emotions are associated with up to 52% of market returns and 67% of market uncertainty during these market crises, and provide general evidence that investor emotional dynamics may be time and context invariant.Journal of Behavioral Financ
Wind energy diffusion in developing countries
Get PDFZawwar, Imran - Associate SupervisorWind energy is valuable, but many developing and emerging economies (DEEs)
do not utilize their substantial wind potential. The objective of this research is to
understand wind energy diffusion with the aim to promote wind energy in
underdeveloped areas for sustainable benefits of both country and wind industry.
The literature review and meta-analysis identified 259 factors that influenced wind
energy diffusion. A novel conceptual framework that describes wind energy
diffusion was developed, dissecting factors that influence wind diffusion into
factors related to the desire for wind energy, factors related to the mechanism of
change and disturbing factors. Regarding DEEs, the meta-analysis showed
indications of the importance of economic factors and, opposing expectations,
environmental factors appear not to drive wind energy growth.
Based on path creation theories but using binary logistic regression as a novel
quantitative approach, the empirical study explored the factors influencing early
wind energy diffusion. Key indicated drivers appeared to be climate adaptation,
vested interests (fossil fuels and hydropower), and the business case potential.
Regarding DEEs, a negative business case potential formed a key barrier. Novel
market entry strategies for the wind power Original Equipment Manufacturer
(OEM) are to collaborate with vested power producers rather than compete and
promote wind for climate adaptation instead of climate change mitigation.
Most high wind potential countries have installed less than 500 MW of wind power
capacity (commercialization threshold). The remaining countries have on
average 20596 MW of wind capacity installed per country. The lagging wind
adopting countries were assessed on their probability to adopt commercial wind
in the near future, by using a novel quantitative path creation forecasting method.
Passive entry, passive waiting, active entry and active waiting were defined as
suggested market entry and development strategies for the wind OEM.Doctor of Business Administratio
Thermal material property evaluation using through transmission thermography: a systematic review of the current state-of-the-art
Get PDFDetermining thermal material properties such as thermal diffusivity can provide valuable insights into a material’s thermal characteristics. A well-established method for this purpose is flash thermography using Parker’s half-rise equation. It assumes one-dimensional heat transfer for thermal diffusivity estimation through the thickness of the material. However, research evidence suggests that the technique has not developed as much as the reflection mode over the last decade. This systematic review explores the current state-of-the-art in through-transmission thermography. The methodology adopted for this review is the SALSA framework that seeks to Search, Appraise, Synthesise, and Analyse a selected list of papers. It covers the fundamental physics behind the technique, the advantages/limitations it has, and the current state-of-the-art. Additionally, based on the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework, a specific set of inclusion and exclusion criteria was determined. This resulted in a final list of 81 journal/conference papers selected for this study. These papers were analysed both quantitatively and quantitatively to identify and address the current knowledge gap hindering the further development of through-transmission thermography. The findings from the review outline the current knowledge gap in through-transmission thermography and the challenges hindering the development of the technique, such as depth quantification in pulsed thermography and the lack of a standardised procedure for conducting measurements in the transmission mode. Overcoming some of these obstacles can pave the way for further development of this method to aid in material characterisation.Engineering and Physical Sciences Research Council (EPSRC), grant number EP/P027121/1Applied Science
Knowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case
Get PDFSpare part inventory management (SPIM) in the railway sector highly demands reliability and transparency for decentralized inventory control. Optimal SPIM should ensure the availability of needed spare parts for a service request, considering the frequency of use and criticality criterion for effective maintenance. Additive manufacturing (AM) technologies enable costeffective production of small batch sizes often required for spare parts. However, critical component-specific information is often unstructured within engineering drawings (ED), making digital processing, and linking to existing data from enterprise resource planning (ERP) and maintenance management systems difficult. To ensure effective maintenance logistics, this paper introduces a knowledge graph (KG) that can facilitate i) interlinking multiple sources through data integration and ii) establishing a semantic data hub, thus serving as a backbone for automated assessment of component's suitability for AM.
The proposed KG-based approach merges relevant (existing) ontologies, multi-structured data from ED, ERP system information, and external data sources. The approach is developed and evaluated in real-world use-cases in cooperation with the Austrian railway and public transit industry.The authors would like to thank and acknowledge Austrian Research Funding Agency (FFG) for supporting this research through AM4Rail project.12th International Conference on Through-life Engineering Services – TESConf202
Pharma 4.0: a deep dive top management commitment to successful Lean 4.0 implementation in Ghanaian pharma manufacturing sector
Get PDFThe primary aim of this study is to assess the significance of top management commitment in the context of Lean 4.0 implementation within the pharmaceutical manufacturing industry in Ghana. The study seeks to understand and evaluate the overall effectiveness and achievements associated with adopting Lean 4.0. Employing a positivist mindset, the research utilizes an explanatory quantitative research design and a survey technique. Data collected from 181 employees of pharmaceutical companies in Ghana undergo analysis using SmartPLS (version 4) and IBM SPSS version 26. The study employs a combination of descriptive statistics to summarise data characteristics and inferential statistics to test various hypotheses related to Lean 4.0 adoption. The analysis reveals that the successful integration of lean methods and Industry 4.0 technologies requires meticulous management. Simultaneously, individual implementations of lean principles and Industry 4.0 technologies positively impact business performance. Surprisingly, the study does not observe a substantial positive influence of Lean 4.0 on corporate performance, suggesting that immediate improvements in efficiency or profitability may not result from the adoption of this framework. This research contributes to the field by highlighting the need for careful management in integrating lean methods and Industry 4.0 technologies. It also emphasizes the positive impacts of lean principles and Industry 4.0 technology on business performance. The unexpected finding regarding the lack of immediate improvements in corporate efficiency or profitability with Lean 4.0 adoption prompts considerations of initial implementation challenges or the organization's need for time to adapt to this integrated approach.Heliyo
Medium entropy alloys for biomedical applications
Get PDFWinner of Best Paper Award.High entropy alloys (HEAs) is a rapidly emerging class of metallic materials consisting of four or more elements in equimolar or quasi-equimolar compositions. These alloys often have simple crystal structures and tailorable properties attracting significant interest for different applications. Common metallic materials for orthopaedic and dental implants include stainless steel, Co-Cr and Ti- alloys. Although these materials are widely in use, issues relevant to biocompatibility and suspected toxicity and the elastic modulus mismatch compared to that of hard tissue have been raised in recent years. High entropy alloys specifically designed for bio-medical applications can offer solutions to overcome these limitations. Bio-HEAs have emerged in the last couple of years and currently receive increasing scientific attention. In this work, we discuss on the design of new entropic alloys using only non-toxic elements such as Ti, Zr, Nb, Ta and Mo. We use a systematic approach to investigate the effect of additional elements on the microstructure and properties of the alloys starting from the binary Ti-Nb and extending to the ternary Ti-Zr-Nb, the quaternary Ti-Zr-Nb-Ta and the Ti-Zr-Nb-Ta-Mo alloy. The alloy design is building on previous work on beta Ti- alloys which has shown promising trends for reducing the elastic modulus of implant materials. The alloys were produced by arc-melting and suction casting under Ar inert atmosphere. X-ray diffraction, and scanning electron microscopy were employed to reveal their crystal structure and microstructure. respectively. The developed alloys exhibit BCC crystal structure and a dendritic microstructure in their as-cast condition. The addition of Zr and Mo was found to increase the hardness of the alloys.12th International Conference on Through-life Engineering Services – TESConf202