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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.

Abstract This paper deals with the numerical study of hydrogen-diesel dual fuel engine characteristics under various diesel injection strategies. Here, CONVERGE CFD software package is used to simulate a hydrogen diesel dual fuel engine, under split injection of diesel. The combustion and spray is modeled using coupled solution of chemical kinetics and fluid mechanics (CFD) equations. The study was performed at four strokes direct injection compression ignition engine. The engine speed (1500 rpm) and compression ratio (19.5:1) remain constant throughout the study. The developed model was validated against already published experimental data of 18.5% hydrogen energy share. The simulation result showed that 8° bTDC was optimum injection timing for single injection case and 16° bTDC was optimum injection timing for pilot fuel injection and 10% pilot mass was optimum pilot mass amount for minimum NOx, soot, CO and HC emissions and higher gIMEP.

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

Abstract New exogenous cis,cis-muconic acid biosynthetic pathway genes were expressed in Saccharomyces cerevisiae. The xylose isomerase gene from Bacteroides valgutus and pentose phosphate pathway genes from S. cerevisiae were overexpressed in the yeast strain. The strain was further modified by the overexpression of gene Aro1 (with a stop codon of AroE) and a feedback-resistant Aro4opt mutant gene from S. cerevisiae. Under oxygen-limited conditions, it produced 65 mg/L cis,cis-muconic acid from xylose. Co-fermentation of 88 g/L glucose and 50 g/L xylose generated 54 g/L ethanol and 248 mg/L cis,cis-muconic acid. Under aerobic conditions, muconic acid titer reached 424 mg/L. With the supplement of 1 g/L catechol, 1286 mg/L muconic acid was produced. Fermentation of an oil palm empty fruit bunch hydrolysate resulted in 31.3 g/L ethanol and 53.4 mg/L muconic acid. This is the first report on the production of muconic acid from lignocellulosic biomass hydrolysate using a recombinant xylose-fermenting yeast.

Hydrogen is a zero emission alternative gaseous fuel generally used in internal combustion engine with single fuel or duel fuel mode. In this work the Hydrogen is introduced in inlet manifold in addition to main diesel fuel used in the engine. The different flow rate of hydrogen fuel is used in this work are from 2 lpm to 10 lpm at 2 bar pressure. Here the single cylinder, direct injection, diesel engine with 1500 rpm rated speed is used for test. In addition to hydrogen, the exhaust gas also introduced in the inlet manifold with various percentages namely 10% and 20%. The engine is loaded with eddy current dynamometer .The engine performance and emissions of various combination of hydrogen flow rate and exhaust gas recirculation (EGR) were analyzed. The result showed that in 8 lpm hydrogen flow rate without EGR the BTE increased and BSFC decreased. At the same condition the HC, CO emissions reduced and NOx emission is increased. But NOx emission with 10% and 20% EGR is reduced.

The aim of this study is to augment thermal transport in latent heat thermal energy storage (LHTES) system by the optimum allocation of metal foam-phase change material (PCM) composite. This study ...

This article demonstrates how a cultural reading of consumption that focuses on the meaning and materiality of domestic indoor microclimates can contribute to conceptual developments in the field of practice theory that refocus attention on cultural patterns, including prevailing norms and prescriptions regarding indoor temperature and thermal comfort. Drawing on evidence collected during a research-led change initiative that encouraged people to reduce energy use in the home by lowering indoor temperature to 18°C, we deploy the heuristic device of “indoor microclimate as artifact” to show how the manifestation of this new artifact initiated significant changes in everyday practices that revolve around heating. We observe that these changes may also spill over into the public sphere – from home to workplace. By making the microclimate a tangible and visible thing, we describe how people appropriate and appreciate this new object of consumption, what it says about different bodies in diverse and bounded spaces, and what the artifact as a commodity reveals about broader systems of heating and energy provision, and associated actors. Due to the increasing spread of central heating and the growing importance of complex technological devices to monitor and control indoor temperature, heating is no longer a practice in and of itself for many urban dwellers in Europe. However, when people appropriate the indoor microclimate, new heating-related practices emerge that can lead to energy sufficiency. We thus argue that by deliberately “materializing” domestic indoor microclimate as part of a change initiative, more sustainable forms of energy use can be made to matter.

Abstract Concerning both the activity and stability of the promising solar-driven Ta3N5-based photoanodes for photoelectrochemical water splitting, the strategy for simultaneously promoting charge separation, enhancing catalytic activity and also improving the resistance to self-oxidation is highly desirable and actively pursued. In this study, a novel dual co-catalyst shell consisting of a continuous CoPi layer at the bottom and many non-continuous Co(OH)2 islands at the top of the CoPi layer is designed to meet the strict requirements for efficient Ta3N5 photoanodes. As a result of the synergistic effects of such a shell in collectively addressing the concerns, the constructed photoanode of CoPi/Co(OH)2-Ta3N5 nanorod arrays show the remarkably enhanced photoelectrochemical water splitting performance compared with the photoanodes with single co-catalyst. The results demonstrated in this study are expected to shed some light on constructing efficient photoelectrodes of the light absorbers that have wide absorption range but low resistance to self-oxidation.