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Microalgae have tremendous potential for producing liquid renewable fuel. Many methods for converting microalgae to biofuel have been proposed; however, an economical and energetically feasible route for algal fuel production is yet to be found. This paper presents a review on the comparison of the most promising conversion pathways of microalgae to liquid fuel: hydrothermal liquefaction (HTL), wet extraction and non-destructive extraction. The comparison is based on important assessment parameters of product quality and yield, nutrient recovery, GHG emissions, energy and the cost associated with the production of fuel from microalgae, in order to better understand the pros and cons of each method. It was found that the HTL pathway produces more oil than the wet extraction pathway; however, higher concentrations of unwanted components are present in the HTL oil produced. Less nutrients (N and P) can be recovered in HTL compared to wet extraction. HTL consumes more fossil energy and generates higher GHG emissions than wet extraction, while the production cost of fuel from HTL pathway is lower than wet extraction pathway. There is considerable uncertainty in the comparison of the energy consumption and economics of the HTL pathway and the wet extraction pathway due to different scenarios analysed in the assessment studies. To be able to appropriately compare methodologies, the conversion methods should be analysed from growth to upgradation of oil utilising sufficiently similar assumptions and scenarios. Based on the data in available literature, wet oil extraction is the more appropriate system for biofuel production than HTL. However, the potential of alternative extraction/conversion technologies, such as, non-destructive extraction, need to be further assessed.

In recent years, sustainability concerns have gained increasing attention among countries and stakeholders worldwide. Towards the transition to sustainable rural development, the rural web framework (RWF) has become a consistent tool. Indicators from the RWF have been used to explore sustainable rural development for decision-making tasks, which improves the social, economic, and environmental performance of rural regions. However, the application of the RWF in studies is on the decline. Furthermore, there is a lack of literature reviews on the importance of the RWF and its relationship with different facets of sustainable development. We conducted a systematic literature review (SLR) (a) to explore how studies have used the RWF in the context of sustainable development and (b) to identify areas for further research. This study found that the RWF has mostly been used in developed countries, with fewer applications in developing countries. We suggest that there should be increased application of the RWF, particularly in developing countries, to broaden the rural web–sustainable development discourse and its relevance. This paper presents several areas where the indicators of the RWF can be applied to illustrate their relevance for policy decisions towards the achievement of the sustainable development goals (SDGs).

Background: Minimizing raw salt and increasing fruit consumption are important factors for controlling blood pressure. The study aimed to investigate the following associations: (i) the association between socio-demographic characteristics and awareness of, and attitudes towards, raw salt and fruit consumption and (ii) the association between salt and fruit consumption and blood pressure. Methods: In a cluster-RCT, 307 adults, aged 30 to 75 years, with hypertension were recruited in 2021. Blood pressure was the primary outcome, and knowledge, attitudes and intakes of raw salt and fruit were secondary outcomes. Results: Of the participants, 271 (78.5%) consumed raw salt. More than 80% of the participants knew that reduced raw salt was good for controlling blood pressure and almost everyone knew that fruits and vegetables were good for health. Despite this, 95% of the participants had a habit of eating fruit irregularly. A lower proportion (64%) of the participants having at least a secondary school certificate (SSC) consumed raw salt, compared with those having an education level SSC (82%), p = 0.002. Blood pressure was not significantly different for participants, irrespective of raw salt or fruit consumption. Conclusions: Raw salt consumption among rural people was high and regular fruit consumption was deficient. They intended to reduce raw salt consumption. Appropriate intervention programs should be implemented to reduce salt consumption and increase fruit consumption.

Abstract This study evaluates the performance of 75 clear-sky global irradiance models against 75 ground stations worldwide covering five major Koppen-Geiger climate classifications and overall global performance. After quality control, clear-sky detection and data-availability criteria, there are 4.36 million 1-min valid global horizontal irradiance (GHI) data points for evaluation. This study represents the most encompassing evaluation of its kind in terms of number of models assessed, number of ground stations used, most consistent selection of input variables in terms of temporal and spatial resolution, also using the most rigorous and fair performance assessment criteria and suitable ranking system. A statistically rigorous Principal Component Analysis (PCA) ranking procedure is proposed to replace the conventional ordering method based on single and simple statistics. In particular, it is demonstrated that all 13 error metrics contribute to the variance of GHI estimation and thus must all be accounted for in the ranking procedure. The best performing models in each climate zone of equatorial, arid, temperate, cold and polar are REST2v9.1, REST2v9.1, MAC2, REST2v5 and CLS, respectively. Globally, the top three performing models are MAC2, REST2v5, and REST2v9.1. Many models appear to suffer from over-fitting empirical relationships to training data, resulting in inconsistent worldwide performance. Furthermore, six different formulations of the Linke Turbidity factor (TL) are evaluated through its application to five clear-sky models that require it as an input. The best globally performing TL formulations are either by Ineichen or Gueymard when combined with four of the five Linke-dependent clear-sky models, whilst the Grenier formulation performs best with the Ineichen & Perez model. Significant performance variation is observed depending on the TL formulation selected, hence, careful selection is required for an optimal application. The model codes are available in R [1].

Abstract There are instances in remote areas where heat is being wasted, e.g., in internal combustion, engines, etc. Some of this heat can be recovered to produce distilled water in solar stills. The solar still replaces the cooling tower, ponds, or radiators normally used to control the engine temperature. The diesel cooling water in such a system remains separate from the saline water in the solar still. The advantages of using such a system compared with a conventional solar still are: 1. (a) water costs are very much reduced 2. (b) the area occupied is much less, i.e., about 1 5 th 3. (c) production has much less seasonal variation 4. (d) the efficiency of the solar still is improved due to the higher operating temperatures. From experiments conducted at Highett using a Mk VI solar still fitted with a simple heat exchanger and a separate electrically-heated source of hot water to simulate the waste heat, design data are not available for application to working systems. The information required to match a solar still to a diesel's cooling requirement is: 1. (a) engine efficiency 2. (b) hourly fuel consumption 3. (c) hourly solar radiation 4. (d) hourly ambient temperatures. A by-product of this work has been the production of a “solar water heater” which costs less than that of the cheapest conventional system. This “solar” hot water system uses a heat exchanger similar to what is used to transfer the waste heat to the saline water. It is envisaged to have hot water productions approximately the same as the distilled water productions. The influence of hot water production on the output of the waste heat solar still is discussed.

Abstract In Australia, the government spending on public buildings’ energy and water consumption is considerable; however the building energy and water retrofit market potential has been diminished by a number of barriers, especially financial. In contrast, in other advanced economies there are several reported financing strategies that have been shown to accelerate retrofit projects implementation. In this study, a coupled Bayesian Network – System Dynamics model was developed with the core aim to assess the likely influence of those novel financing options and procurement procedures on public building retrofit outcomes scenarios in the Australian context. A particular case-study focusing on Australian public hospitals was showcased as an example in this paper. Stakeholder engagement was utilised to estimate likely preferences and to conceptualise causal relationships of model parameters. The scenario modelling showed that a revolving loan fund supporting an energy performance contracting procurement procedure was preferred. Subsequently, the specific features of this preferred framework were optimised to yield the greatest number of viable retrofit projects over the long term. The results indicated that such a financing scheme would lead to substantial abatement of energy and water consumption, as well as carbon emissions. The strategic scenario analysis approach developed herein provides evidence-based support to policy-makers advocating novel financing and procurement models for addressing a government’s sustainability agenda in a financially responsible and net-positive manner.

Exhaust hoods are commonly used to capture all emissions from stationary combustion systems that are open to the environment, such as residential heaters or stoves. For experimental purposes, emissions are sampled at one, or more, discrete locations downstream in the exhaust duct. Point-wise measurements in the duct are often taken with the assumption that the emissions are homogeneously distributed across the duct cross-section, because the flow is turbulent and therefore believed to be thoroughly mixed. However, the length of such systems is rarely sufficient to ensure fully-developed flow, and the actual homogeneity is seldom assessed. In the present work the mixing within the duct is investigated by simulating the emissions distribution within various hood and duct configurations. The simulations include a straight duct with and without baffles and two different exhaust hood configurations, namely at the Stove Testing Lab at Lawrence Berkeley National Laboratory (LBNL) and at the University of Adelaide that meet standard requirements. The air flow in the ducts was simulated using Reynolds-averaged (RANS) turbulence modelling, with carbon monoxide (CO) as a representative combustion product, injected at three locations in the straight duct and two locations (centre and side) in the exhaust hoods. Simulations predict that, in isolation, neither a straight duct without baffles, nor a hood with a 90° elbow followed by a straight duct without baffles, provide sufficient mixing to achieve a near uniform distribution of CO at the sampling locations. However, simulations show that adequate mixing of dilution air and CO is achieved with baffles-induced flow detachment and recirculation, not from turbulent mixing in the straight section of the duct itself. The simulations also suggest that elbows, baffles, expansions or other geometrical features are needed to induce thorough mixing. For example, in the Stove Testing Lab at LBNL, flow disturbance is induced by an expansion into a larger diameter straight duct immediately downstream of the hood and the 90° elbow. Although these two systems demonstrate sufficient mixing of CO within the exhaust, the RANS simulations in this study suggest that other systems relying solely on mixing within a specified duct length (viz. 8–12 diameters) may not be sufficient. Refereed/Peer-reviewed

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.