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Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

• Apply python to interface with plaxis software to simulate the embankment settlement for a case study in Australia. • Develope a GEP-based prediction model to estimate the settlement of ground improvement. • Conduct a parametric study to examine the effects of input variables on the settlement of the ground. • Optimize the design of CMC diameters and CMC spacings for specific ground conditions. This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making. [ABSTRACT FROM AUTHOR]

Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy. • We predict groundwater levels based on stream flow and rainfall data. • Our framework uses Bayesian data imputation and deep learning for multi-step prediction. • We examine the groundwater fluctuations at various boreholes located in Australia. • Results show a fine-tuned regional model with rainfall data yielded the best accuracy. • We find that LSTM models outperformed CNNs using the multivariate approach. [ABSTRACT FROM AUTHOR]

Emerging industries and technologies are made up of objective, practical requirements and ideas that encompass broad hopes for what future newness might enable. The bioeconomy is one such emerging industry. Its recent evolution has involved a complicated and dynamic mix of promises, reality checks and subsequent ambivalences. This article frames and investigates the emerging algae-based bioplastics industry as one niche within this broader system envisaged for social, technological, economic, political and ecological change. As such, our paper presents two categories of knowledge that in practice interrelate: 1) specific and practical recommendations that can assist a future algal-based bioplastics industry to develop in Australia in ways that are conscious of socio-ecological and socio-cultural dimensions, and 2) critical extensions that go beyond the attributes of newness, impact, suddenness and universality often emphasised in both popular and scientific research into emerging green technologies. In particular, our analysis highlights the importance of specific temporal, tonal and spatial factors when framing the contextual factors associated emerging industries, technologies and the different pathways for change they might help support. • Pathways and enabling factors for a algal-based bioplastics industry in regional Australia are presented. • The 'social idea' of the bioeconomy is analysed with reference to algal-based bioplastics. • The algal-based bioplastics industry is contextualised in relation to transition pathways literatureon the bioeconomy. • Themes and recommendations are synthesised from literature review and semi-structured interviews. [ABSTRACT FROM AUTHOR]

In recent years, many FinTech start-up firms have poured into the FinTech landscape. Since FinTech leaders (including founders and chief executive officers (CEOs)) have a major impact on the performance of their respective firms, research studies on FinTech leaders are indispensable. This paper discusses our study to investigate three major dimensions (education, work experience, and eagerness of self-employment) of FinTech leaders in an Australian context. We also compare these dimensions between FinTech founders and CEOs. Overall, we found that: (a) The majority of FinTech leaders are degree holders and one-third of them have a master's degree. (b) Only about one-third of leaders with a master's degree hold an MBA. (c) Only 19.4% and 11.1% of degree-holding leaders were graduated from the IT and finance discipline, respectively. (d) Only 19.0% of leaders have previous IT work experience and the mean years of such experience is only 2.0. (e) 54.8% of leaders have previous finance work experience and the mean years of such experience is 6.7. (f) When compared with CEOs, founders are more likely to hold a master's degree and are more eager to be self-employed. [ABSTRACT FROM AUTHOR]

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model's parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. • A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform. • A new combination of chaotic adaptive exploration and dynamic archive is introduced. • Effective and practical discretisation method improved performance of hybrid platform. • The performance of the proposed optimiser is validated on four real sea studies. [ABSTRACT FROM AUTHOR]

The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

This paper presents experimental and numerical investigations into the post-fire cross-section compressive behaviour and resistances of circular recycled aggregate concrete-filled stainless steel tube (RACFSST) stub columns. Twelve stub column specimens, fabricated from concretes with three recycled coarse aggregate replacement ratios (0%, 35% and 70%), were tested after exposure to the ISO-834 standard fire for 0 min (i.e. at ambient temperature), 15 min, 30 min and 45 min. The experimental investigation included heating and cooling of specimens, cylinder tests and post-fire tensile coupon tests and stub column tests. The test results, including load–end shortening curves, failure loads and failure modes, were fully reported and the initial compressive stiffnesses and confinement effect were discussed. The numerical investigation was subsequently conducted, where thermal and mechanical finite element models were developed and validated against the test results and then used to perform parametric studies to generate further numerical data. Given the absence of design codes for stainless steel–recycled aggregate concrete composite structures after exposure to fire, the relevant design rules for circular natural aggregate concrete-filled carbon steel tube stub columns at ambient temperature, as set out in the European code, Australia/New Zealand standard and American specification, were assessed, using post-fire material properties, for their applicability to circular RACFSST stub columns after exposure to fire, based on the test and numerical data. The assessment results generally revealed that the European code and the Australian/New Zealand standard led to an acceptable level of design accuracy and consistency, while the American specification resulted in rather conservative and scattered post-fire cross-section compression resistance predictions. • The compressive behaviour of circular RACFSST stub columns after exposure to fire is investigated. • Post-fire compression tests on twelve circular RACFSST stub column specimens are conducted. • The influences of heating durations and RCA replacement ratios are analysed. • Thermal and mechanical FE models are validated against test results and then used to conduct parametric studies. • The relevant design codes are assessed against the test and FE data. [ABSTRACT FROM AUTHOR]

Australia is a long-term key partner of China in coal trading, which is famous for high-quality coal and its price advantage. However, affected by the evolution of international political and economic situation, China has issued a coal ban on Australian coal since the end of 2020. The potential environmental consequences of this coal ban remain unclear. So, this paper quantifies the ban's impact on air pollution, human health and climate change by simulating the emission difference between the port scenario (simulating the emissions due to Australian coal import under business as usual) and the mine scenario (simulating the emissions due to domestic coal mining that converted from import share of Australian coal), and monetizes the losses into economic costs for direct comparison. The results illustrate the emission of SO 2 , VOCs and GHG in mine scenario grows up to 102, 12 and 6 times the amount of that in port scenario, mainly deriving from coal mining process in the North China Plain and Yunnan-Guizhou Plateau. It shows that the potential alternative of replacing import with mining leads to PM 2.5 pollution exacerbation and O 3 pollution abatement, which causes 148 (95 % CI: 101–183) non-communicable diseases and respiratory disease-related deaths in total. As for GHG, the emission of BC in transportation process and fugitive methane in coal mining process contribute to an impressive effect on warming potential with few quantities, which is worth noting in control process. In total, the environmental deterioration resulted in an economic loss of approximately 934 million yuan. The undiminished demand for high-quality industrial coal as well as growing carbon tax could make the economic value of the coal ban's environmental consequences more considerable in the near future. This research emphasizes the contradiction between economic development and environmental improvement caused by excessive dependence on coal and addresses a critical knowledge gap in assessing the ban's environmental impact by economic value. The result directly demonstrates the environmental consequences of the potential mining-replacing-import alternative and helps better understand environmental influences on the Chinese government's decision. [ABSTRACT FROM AUTHOR]

Microplastic (MP) can significantly affect soil behaviour and the ecosystem. This paper presents an experimental study to investigate the effects of MP contamination and leachate exposure on the desiccation cracks, hydraulic conductivity, and water retention properties of the natural black clay. The leachate was from a landfill in Australia. The black clay was incorporated with up to 2.0% MPs by weight (w/w) with diverse dimensions and mixed with water/leachate. The measured properties include saturated hydraulic conductivity (k sat), soil-water characteristic curves, moisture evaporation rates, and crack intensity factors. The results suggest that the inclusion of MPs significantly increases k sat , and this increase is more obvious for soils with larger dimensions and contents of MPs, e.g., k sat of the black clay with 2.0% of 500 μm MP increases significantly by 206% (p < 0.05). The black clay exposed to leachate exhibits a slight increase in k sat due to the low viscosity of leachate. The existence of MPs decreases the residual moisture contents and air-entry pressures, and so does the water retention capacity (v/v %) of the black clay. The exposure to leachate increases the air-entry pressures by 6.0%–15.8% of the clay. The evaporation rates increase with the dimensions and concentrations of MPs. The highest evaporation rate (0.96 g/h) can be observed in samples exposed to 2.0% 500 μm MP with water addition. For all samples, the crack intensity factors increase when MP content is between 0.2% and 1.0% and decreases slightly after that. After being exposed to leachate, the evaporation rates and crack intensity factors of the black clay are decreased by 2.4%–12.6% and 3.6%–13.7%, respectively. • Contamination of MPs increases the saturated hydraulic conductivity of clays. • Mature leachate does not significantly affect permeability or flow channel of clays. • MPs affect residual moisture contents and air-entry pressure of clays. • Existence of MPs affects the evaporation rate and desiccation crack of clays. • Leachate affects air-entry pressure, evaporation rate, and crack intensity of clays. [ABSTRACT FROM AUTHOR]

Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation. • Spatial predictions of wildfire spread are frequently used but not always evaluated. • We synthesised fifteen loss functions for evaluating predicted vs observed wildfire spread. • There is not a 'best' loss function, it depends on simulation use and interpretation. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]