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With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing.

We present a rotational terahertz imaging system for inline nondestructive testing (NDT) of press sleeves for the paper industry during fabrication. Press sleeves often consist of polyurethane (PU) which is deposited by rotational molding on metal barrels and its outer surface mechanically processed in several milling steps afterwards. Due to a stabilizing polyester fiber mesh inlay, small defects can form on the sleeve’s backside already during the initial molding, however, they cannot be visually inspected until the whole production processes is completed. We have developed a fast-scanning frequenc-modulated continuous wave (FMCW) terahertz imaging system, which can be integrated into the manufacturing process to yield high resolution images of the press sleeves and therefore can help to visualize hidden structural defects at an early stage of fabrication. This can save valuable time and resources during the production process. Our terahertz system can record images at 0.3 and 0.5 THz and we achieve data acquisition rates of at least 20 kHz, exploiting the fast rotational speed of the barrels during production to yield sub-millimeter image resolution. The potential of automated defect recognition by a simple machine learning approach for anomaly detection is also demonstrated and discussed.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

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A digital microfluidic modular interface (chip-to-chip interface) which possesses an electrode with an orifice to vertically transport core–shell droplets is presented. The electrodes were geometrically designed to promote droplet deformation and suspension. The droplets were then applied with an electrical potential for insertion into and passage through the orifice. The concepts were tested with three types of droplets at the volume of 0.75~1.5 μL, which is usually difficult to transfer through an orifice. The integration of electrowetting on dielectric (EWOD) with paper-based microfluidics was demonstrated: the droplet could be transported within 10 s. More importantly, most of the core droplet (~97%) was extracted and passed through with only minimal shell droplets accompanying it.

The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation.

The aim of the study was to assess leaching of Cl−, SO42−, and PO43− from ashes formed in household furnaces. The ashes were obtained following the combustion of conventional fuels, namely wood and hard coal, and alternative fuels with various fractions of municipal waste in a household boiler. Aqueous leachates of the ashes were used to determine concentrations of chlorides by titration (Mohr’s method) (21.3–3049.6 mg/dm3), sulphates by the gravimetric method (12.2–244.1 mg/dm3), and phosphates by spectrophotometry (0.01–67.2 mg/dm3). It was found that co-combustion of municipal waste with plastic-coated paper cartons, diapers, or a mixed waste fraction leaves the greatest amount of ashes on the furnace grate. The highest amounts of Cl−, SO42−, and PO43− were leached from ashes generated from burning a mix of wood and coals, or wood alone (different species). The addition of municipal waste to the process of burning the conventional and alternative fuels studied did not significantly increase Cl−, SO42−, and PO43− content in aqueous extracts of ashes, the exception being diapers and plywood. In light of the study results, it was concluded that all the ashes could be reused (as an additive to concrete) except for the ash generated from the combustion of a mixed municipal waste fraction and coal (due to the content of Cl−) and diapers (due to the content of PO43−). It was demonstrated that Cl−, SO42−, and PO43− content in the entire set of samples and in individual ash groups is highly heterogeneous and variable.

Cattle biomass (CB or manure from cattle) is proposed as reburn fuel under slightly fuel-rich conditions. The CB includes wastes from cattle feedlots (cattle grown in feedlots to slaughter weights of 450–640 kg) termed as Feedlot Biomass (FB) and cattle wastes from dairy farms termed as Dairy Biomass (DB). NOx emissions from coal-fired power plants can be reduced by using pure CB and Coal:CB mixtures as reburn fuels (10~30% by heat) injected after the primary combustion region. Experiments with Coal:CB mixtures as reburn fuels were performed using the 30 kW burner facility. Part I deals with results from experiments using pure FB and Coal:FB blends as reburn fuels while Part II presents results on extent of NOx reduction using pure DB and Coal:DB blends as reburn fuels (RF). In the current work, results on NOx emission are presented with FB and Coal:FB blends as RF. The parametric studies include: equivalence ratio in reburn zone (ERRBZ), vitiated air, angle of reburn nozzles, presence and absence of heat exchangers (HEXs), and baseline NOx concentration. The optimum operating conditions for FB reburning were found to be conditions with vitiation at ERRBZ = 1.1 with 45° upward counter-current injection in the presence of HEXs. NOx emissions were reduced by as much as 96%.

Milk is a necessity for human life. However, it is susceptible to contamination and adulteration. Microfluidic analysis devices have attracted significant attention for the high-throughput quality inspection and contaminant analysis of milk samples in recent years. This review describes the major proposals presented in the literature for the pretreatment, contaminant detection, and quality inspection of milk samples using microfluidic lab-on-a-chip and lab-on-paper platforms in the past five years. The review focuses on the sample separation, sample extraction, and sample preconcentration/amplification steps of the pretreatment process and the determination of aflatoxins, antibiotics, drugs, melamine, and foodborne pathogens in the detection process. Recent proposals for the general quality inspection of milk samples, including the viscosity and presence of adulteration, are also discussed. The review concludes with a brief perspective on the challenges facing the future development of microfluidic devices for the analysis of milk samples in the coming years.

Phenolic compounds are particularly dangerous due to their ability to remain in the environment for a long period of time and their toxic effects. They enter in the environment in different ways, such as waste from paper manufacturing, agriculture (pesticides, insecticides, herbicides), pharmaceuticals, the petrochemical industry, and coal processing. Conventional methods for phenolic compounds detection present some disadvantages, such as cumbersome sample preparation, complex and time-consuming procedures, and need of expensive equipment. Therefore, there is a very large interest in developing sensors and new sensing schemes for fast and easy-to-use methods for detecting and monitoring the phenolic compound concentration in the environment, with special attention to water. Good analytical properties, reliability, and adaptability are required for the developed sensors. The present paper aims at revising the most generally used optical methods for designing and fabricating biosensors and sensors for phenolic compounds. Some selected examples of the most interesting applications of these techniques are also proposed.

Although replacing biomass, (e.g., wood chips and pellets), with thinning wood and herbaceous biomass is eco-friendly and economically advantageous, their direct utilization in plant boilers is associated with ash-related challenges, including slagging and fouling. The aim of this study is to determine the effects of ash removal treatment (ashless biomass (ALB)) in the context of solid fuel power plant boilers. Ash was removed via neutralization of metal ions and carboxylic acids contained in the biomass ash. The ash removal rate of K, Na, Cl was indicated by assessing the total biomass before and after ash removal treatment, via XRF analysis. Co-combustion with sub-bituminous coal and ALB-treated biomass was analyzed using a drop tube furnace and revealed that NOx and SOx values converged converge toward an approximate 10 ppm error, whereas the Unburned Carbon (UBC) data did not exhibit a specific trend. Factors associated with slagging and fouling, (capture efficiency (CE), and energy based growth rate (GRE)) were calculated. All biomass samples without pretreatment exhibited V-shaped variation. Conversely, for ashless biomass (ALB) samples, CE and GRE gradually decreased. Thus, the ALB technique may minimize slagging and fouling in a boiler, thus, reducing internal corrosion associated with ash deposition and enhancing the economic operation of boilers.

The cocombustion of coal and pinus sawdust waste is an economically viable and sustainable option for increasing the share of biomass in energy production. This technology also has the potential to reduce the emission of greenhouse gases from existing coal fired power plants. The thermal synergistic effects of cocombusting Hwange bituminous coal (HC) with Pinus sawdust (PS) were thus investigated using thermogravimetric analysis. Fuel blending mass ratios of 100HC, 90HC10PS, 80HC20PS, 70HC30PS, and 100PS under an oxidative atmosphere at three different heating rates of 5, 12.5, and 20 °C/min were used for the experimental setup. Zero to negative synergy was generally observed for the mass loss curves (TG) at different blending ratios. Generally positive synergy was observed with relation to rate of mass loss curves (DTG) for the 80HC20PS and 70HC30PS fuel blends only. The ignition index increased with blending ratio by an average of 42.86%, whilst the burnout index showed a maximum increase of 14.6% at 20 °C/min. However, the combustion index representative of stability showed a decreasing trend generally for all the heating rates. No combustion index produced a linear variation with temperature, though upon evaluation, an optimum mass ratio of 20% pinus sawdust was suggested. The chosen optimum blending ratio demonstrated increased ignition and burnout indexes whilst maintaining the stability of combustion at a reasonable range.

The steelmaking industry requires coke as a reducing agent, as an energy source, and for its ability to hold slag in a blast furnace. Coking coal as raw coke material is very limited. Studying the use of biomass as a mixture of coking coal in the synthesis of biocoke is necessary to reduce greenhouse gas coal emissions. This research focuses on biomass and heating temperature through the coal blending method to produce biocoke with optimal mechanical properties for the blast-furnace standard. The heating temperature of biomass to biochar was evaluated at 400, 500, and 600 °C. The blending of coking coal with biochar was in the compositions of 95:5, 85:15, and 75:25 wt.%. A compacting force of 20 MPa was employed to produce biocoke that was 50 mm in diameter and 27 mm thick using a hot cylinder dye. The green sample was heated at 1100 °C for 4 h, followed by quenching with a water medium, resulting in dense samples. Increasing heating temperature is generally directly proportional to an increase in fixed carbon and calorific value. Biocoke that meets several blast-furnace criteria is a coal mixture with coconut-shell charcoal of 85:15 wt.%. Carbonization at 500 °C, yielding fixed carbon, calorific value, and compressive strength, was achieved at 89.02 ± 0.11%; 29.681 ± 0.46 MJ/kg, and 6.53 ± 0.4 MPa, respectively. This product meets several criteria for blast-furnace applications, with CRI 29.8 and CSR 55.1.

Biomass is one of the most important sources of renewable energy. It is expected that in the coming decades, biomass will play a major role in replacing fossil fuels. The most commonly used biofuels include wood pellet, which is a cost-effective, uniform and easy-to-use material. In view of the growing interest in this type of resource, novel methods are being investigated to improve the quality of pellet. This article presents the results of a laboratory study focusing on wood pellets refined with waste sunflower cooking oil applied by spraying. In this work, authors attempted to modify the energy parameters of wood pellets with the use of waste cooking oil. Addition of waste cooking oil, applied at the rates of 2%, 4%, 6%, 8%, 10% and 12% relative to the weight of pellets, increased the calorific value of the pellets without decreasing their durability. The highest dose of the modifier (12%) on average led to a 12–16% increase in calorific value. In each case, the addition of sunflower oil resulted in decreased contents of ash in the pellets; on average a decrease of 16–38% was observed in the samples treated with the highest dose of the modifier. The treatment led to a higher content of elements affecting the heating value, i.e., carbon and hydrogen, which on average increased by 7.5–12%, and 7.0–10.0%, respectively. The presented method seems to be a promising way of increasing the calorific value of pellets. Further research on refining the method and the possibility of using it in industry is necessary.

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues. ispartof: SENSORS vol:21 issue:15 ispartof: location:Switzerland status: published

Microwave torrefaction of oat hull was conducted to enhance its physicochemical properties. A bench-top reactor with an internal stirrer was used for oat hull pretreatment at temperatures of 225 °C, 255 °C, and 285 °C, and residence times of 3, 6, and 9 min, respectively. Results showed that a high temperature level at 3 min residence time or severe torrefaction increased calorific values by up to 35% of its original value, while decreasing mass yield down to 60.77%. Severe torrefaction further decreased moisture absorption, moisture content, and grinding energy consumption but decreased energy yield and bulk density. Residence time had no significant effect on biomass physicochemical changes; however, production cost may be significantly increased by longer residence times. It was also concluded that increased microwave power levels from 400 to 650 W decreased energy consumption by shortening processing times, resulting in a positive economic impact of the process. Moderate and severe torrefaction significantly enhanced biomass fuel properties, and short residence times are recommended in order to decrease electricity consumption. In addition, microwave pretreatment enhances biomass in a similar way to conventional torrefaction, but at a faster processing time. Moreover, the liquid fraction as a by-product may represent a valuable product for the food industry.

The digestate is a prospective biofertilizer and potential source of income for many biogas plants worldwide. However, its actual impact on the soil properties and biomass yield is still unexploited. The different digestates from eight agricultural biogas plants were researched in terms of their chemical composition and the fertilizing potential. The results obtained from digestate chemical analysis indicate that the digestate biomass had large amount of nitrogen (up to 73 g kg−1 fresh mass) and potassium (up to 25 g kg−1 fresh mass). The value of the digestate was estimated in the range of 2.88–7.89 EUR Mg−1 for liquid digestate and 7.62–13.61 EUR Mg−1 for solid digestate based on the commercial fertilizer market price of nitrogen, potassium phosphorus, organic carbon, Cu, Zn, Fe and Mg. The digestate produced at the 1 MW biogas plant is worth EUR 941–2095 per day in addition to energy sales income. The application of digestate on low-fertility land in areas close to the biogas plant allows the production of up to three-fold more biomass suitable for biogas production. The digestate’s application on semi-natural grass biomass production in the low-fertility soils near the biogas plants could be an alternative strategy for the biogas plant feedstock portfolio diversification.

Microalgae are natural resources of intracellular compounds with a wide spectrum of applications in, e.g., the food industry, pharmacy, and biofuel production. The extracellular polymeric substances (EPS) released by microalgal cells are a valuable bioproduct. Polysaccharides, protein, lipids, and DNA are the main constituents of EPS. This review presents the recent advances in the field of the determinants of the synthesis of extracellular polymeric substances by microalgal cells and the EPS structure. Physical and chemical culture conditions have been analyzed to achieve useful insights into the development of a strategy optimizing EPS production by microalgal cells. The application of microalgal EPS for flocculation and mechanisms involved in this process are also discussed in terms of biomass harvesting. Additionally, the ability of EPS to remove toxic heavy metals has been analyzed. With their flocculation and sorption properties, microalgal EPS are a promising bioproduct that can potentially be used in harvesting algal biomass and wastewater management.

The properties, types, and physical and chemical aspects of pig slurry used in the fermentation process were presented. Characterization of the pig slurry microflora for a controlled biogas production process was performed. A pilot biogas treatment installation was presented on the example of a farm with 1100 Dan Bred fatteners kept in a grate system. The research was carried out to measure the biogas flow rate resulting from the reference pressure in the fermentor. An independent assessment of the amount of biogas and the pressure drop in the skeletal deposit was carried out. The basis for assessing the hydrodynamics of gas flow through the adhesive bed is the flow characteristic, which results from the pressure that forces this flow. In each case, the determination of this characteristic consists in determining the influence of the biogas stream on the value of this overpressure, equivalent to the pressure drop (it is tantamount to determining the total biogas flow resistance through the adhesive bed). The results of the measurements indicate the practical application of pig slurry-a substrate in a polydisperse system for the production of agricultural biogas in the context of renewable energies. The article indicates that the ferment was periodically mixed during the day, together with the fermentation of the ferment with fresh substrate. The tests were conducted for 49 days, thus demonstrating that it is more advantageous to mix the ferment hydrodynamically, obtaining a CH4 level of about 80%.

Humanity’s overreliance on fossil fuels for chemical and energy production has resulted in uncontrollable carbon emissions that have warranted widespread concern regarding global warming. To address this issue, there is a growing body of research on renewable resources such as biomass, of which cellulose is the most abundant type. In particular, the electrochemical reforming of biomass is especially promising, as it allows greater control over valorization processes and requires milder conditions. Driven by renewable electricity, electroreforming of biomass can be green and sustainable. Moreover, green hydrogen generation can be coupled to anodic biomass electroforming, which has attracted ever-increasing attention. The following review is a summary of recent developments related to electroreforming cellulose and its derivatives (glucose, hydroxymethylfurfural, levulinic acid). The electroreforming of biomass can be achieved on the anode of an electrochemical cell through electrooxidation, as well as on the cathode through electroreduction. Recent advances in the anodic electroreforming of cellulose and cellulose-derived glucose and 5-hydrooxylmethoylfurural (5-HMF) are first summarized. Then, the key achievements in the cathodic electroreforming of cellulose and cellulose-derived 5-HMF and levulinic acid are discussed. Afterward, the emerging research focusing on coupling hydrogen evolution with anodic biomass reforming for the cogeneration of green hydrogen fuel and value-added chemicals is reviewed. The final chapter of this paper provides our perspective on the challenges and future research directions of biomass electroreforming.

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

Blood from livestock slaughtering imposes a high organic pollution load and risks. If it is discharged untreated to sewer systems, it increases the organic pollution load on wastewater treatment plants by 35–50%. This paper reviews blood anaerobic digestion. It analyzes the quantities, composition, methane potential reported, microbiology, biochemical pathways of blood protein degradation, environmental and health issues, and strategies suggested to manage them during livestock blood anaerobic digestion. Although challenging, anaerobic digestion of blood as a mono-substrate is possible if the culture-reactor system is controlled based on a complete characterization and understanding of the microbial community and its metabolic activities. Co-digestion of blood and other feedstock proceeds well if the mixtures are well designed. Generally, the specific methane yield from digesting blood alone ranges between zero and 0.45 m3 kg−1 protein, whereas for co-digesting blood and other substrates, the yield varies between 0.1 and 0.7 m3 kg−1 volatile solids. More research is required for microbiology and kinetics, the role of adsorbents, reactor configuration, and culture adaptation during anaerobic digestion of blood to better control the process.

Biodiesel is a renewable energy source which is gaining prominence as an alternative fuel over fossil diesel for different applications. Due to their higher viscosity and lower volatility, biodiesels are blended with diesel in various proportions. B20 blends are viable and sustainable solutions in diesel engines with acceptable engine performance as they can replace 20% fossil fuel usage. Biodiesel blends are slightly viscous as compared with diesel and can be used in common rail direct injection (CRDI) engines which provide high pressure injection using an electronic control unit (ECU) with fuel flexibility. In view of this, B20 blends of three biodiesels derived from cashew nutshell (CHNOB (B20)), jackfruit seed (JACKSOB (B20)), and Jamun seed (JAMNSOB (B20)) oils are used in a modified single-cylinder high-pressure-assisted CRDI diesel engine. At a BP of 5.2 kW, for JAMNSOB (B20) operation, BTE, NOx, and PP increased 4.04%, 0.56%, and 5.4%, respectively, and smoke, HC, CO, ID, and CD decreased 5.12%, 6.25%, 2.75%, 5.15%, and 6.25%, respectively, as compared with jackfruit B20 operation.

The growing interest in processes that involve biomass conversion to renewable energy, such as anaerobic digestion, has stimulated research in this field in order to assess the optimum conditions for biogas production from abundant feedstocks, like agro-industrial wastes. Anaerobic digestion is an attractive process for the decomposition of organic wastes via a complex microbial consortium and subsequent conversion of metabolic intermediates to hydrogen and methane. The present study focused on the exploitation of liquid cow manure (LCM) and cheese whey (CW) as noneasily and easily biodegradable sources, respectively, using continuous stirred-tank reactors for biogas production, and a comparison was presented between single- and two-stage anaerobic digestion systems. No significant differences were found concerning LCM treatment, in a two-stage system compared to a single one, concluding that LCM can be treated by implementing a single-stage process, as a recalcitrant substrate, with the greatest methane production rate of 0.67 L CH4/(LR·d) at an HRT of 16 d. On the other hand, using the easily biodegradable CW as a monosubstrate, the two-stage process was considered a better treatment system compared to a single one. During the single-stage process, operational problems were observed due to the limited buffering capacity of CW. However, the two-stage anaerobic digestion of CW produced a stable methane production rate of 0.68 L CH4/(LR·d) or 13.7 L CH4/Lfeed, while the total COD was removed by 76%.

Iron-based industries are one of the main contributors to greenhouse gas (GHG) emissions. Partial substitution of fossil carbon with renewable biocarbon (biomass) into the blast furnace (BF) process can be a sustainable approach to mitigating GHG emissions from the ironmaking process. However, the main barriers of using biomass for this purpose are the inherent high alkaline and phosphorous contents in ash, resulting in fouling, slagging, and scaling on the BF surface. Furthermore, the carbon content of the biomass is considerably lower than coal. To address these barriers, this research proposed an innovative approach of combining two thermochemical conversion methods, namely hydrothermal carbonization (HTC) and slow pyrolysis, for converting biomass into suitable biocarbon for the ironmaking process. Miscanthus, which is one of the most abundant herbaceous biomass sources, was first treated by HTC to obtain the lowest possible ash content mainly due to reduction in alkali matter and phosphorous contents, and then subjected to slow pyrolysis to increase the carbon content. Design expert 11 was used to plan the number of the required experiments and to find the optimal condition for HTC and pyrolysis steps. It was found that the biocarbon obtained from HTC at 199 °C for 28 min and consecutively pyrolyzed at 400 °C for 30 min showed similar properties to pulverized coal injection (PCI) which is currently used in BFs due to its low ash content (0.19%) and high carbon content (79.67%).

Countries globally are focusing on alternative fuels to reduce the environmental pollution. An example is biodiesel fuel, which is leading the way to other technologies. In this research, the methyl esters of castor oil were prepared using a two-step transesterification process. The respective properties of the castor oil (Ricinus Communis) biodiesel were estimated using ASTM standards. The effect of performance and emission on diesel engines was noted for four various engine loads (25, 50, 75, and 100%), with two different blends (B5 and B20) and at two different engine speeds (1500 and 2000 rpm). The study determined that B5 and B20 samples at 1500 rpm engine speed obtained the same power, but diesel fuel generated greater control. The power increased at 2000 rpm for B5 samples, but B20 samples, as well as diesel, were almost the same values. In the 40–80% range, load and load values were entirely parallel for each load observed from the engine performance of the brake power in all samples.

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models’ adoption in the industry.

The use of renewable energy sources increases the energy self-sustainability of cities, enabling citizens to reduce energy costs, which results in an increase in their standard of living. However, solar energy penetration in Bosnia and Herzegovina, and its capital Sarajevo, is not in line with the possibilities. Furthermore, the Sarajevo Canton is extremely polluted during the winter months because of the use of unacceptable heating fuel. The aim of this paper is to introduce photovoltaic power systems use in heating electrification system. In this paper AQI is calculated based on historical data and the hybrid model EMD-SARIMA for air pollution and a solar production forecast is presented. The methodology was tested in the Sarajevo Canton, taking into account 35,000 households. In order to ensure clean air, renewable electric energy use for household heating should be implemented. The widespread use of inefficient individual heating systems characterized by inefficient and expensive use of firewood and the use of coal in individual furnaces in populated areas are the main problems of internal and urban air pollution in Sarajevo Canton. In order to reduce energy poverty in Sarajevo Canton, the use of a floating photovoltaic power plant located on Lake Jablanica with a capacity of 30 MW and the solar prosumers with capacity of 115 MW to provide the 196 GWh necessary for heating electrification of 35,000 households is implemented in this paper. Finally, based on correlation between AQI forecast and solar production it was calculated that the values of the AQI, considering the application of solar energy during 150 days (five months) in one heating season, have significantly decreased. Also renewable energy sources have a very important role in reducing carbon dioxide (CO2) emissions into the atmosphere and reducing urban pollution. With this approach, households would be heated by renewable electricity, which would make Sarajevo a cleaner, smarter city.

Energy poverty is high up on national and European Union policy agendas. A number of possible indicators to measure the issue have been identified in the literature, but comparable data with European coverage is scarce. The EU Commission thus proposes four independent indicators on the &ldquo, EU Energy Poverty Observatory&rdquo, based on self-reported items from the pan-European surveys on income and living conditions (SILC) and household budgets (HBS). It is of increasing public interest to analyse social impacts of energy policies, and quantify energy poverty indicators also from modelling. This paper first shortly outlines how the expenditure-based indicators using HBS micro data may be directly linked to existing macroeconomic models through their defining variables (energy expenditure and income). As endogenous modelling based on micro data is difficult, the link may be country-specific elasticities. The main contribution of the paper is a systematic in-depth sensitivity analysis of the two indicators to changes in income and energy expenditure following varying patterns in the underlying distributions of the micro data. The results may be used by future soft links to models. The results display sometimes counterintuitive effects. We find that whether these indicators increase/decrease after a change of income or energy expenditure largely depends on the specific country-wise income and energy expenditure distribution between households on a micro-level. Due to their definition, the examined indicators are especially sensitive, when income changes alter the indicator threshold values, which in these cases are the median values in underlying distributions. We discuss these findings and relate them to several indicator shortcomings and potential remedies through changes in indicator definition.

To realize the adaptive grasping of objects with diverse shapes and to capture the joint angles of the finger, a multi degree of freedom (DOF) adaptive finger for prosthetic hand is proposed in this paper. The fingers are designed with three joints. The maximum rotation angle of the finger joints is 90&deg, The angle at which the finger joints bend can be captured. Firstly, the prototype design, forward kinematics and force analysis of phalanges are described in detail. In order to achieve an adaptive motion pattern similar to that of the human hand, this paper investigates the optimization of the torsion spring stiffness coefficient so that the metacarpophalangeal (MCP) joints, proximal interphalangeal (PIP) joints, and distal interphalangeal (DIP) joints of the bionic finger meet a motion ratio of approximately 3:3:1. Then, in order to realize the joint angle measurement in the process of grasping an object, the mechanical-sensor integrated finger joint is designed, and the composition, angle measurement principle and measurement circuit are introduced in detail. Finally, joint angle measurement, movement law evaluation and object grasping experiments are performed to verify the validity of the designed finger. The experimental results show that the root-mean-square (RMS) of the DIP, PIP and MCP angle measurement errors are 0.36&deg, 0.59&deg, and 0.32&deg, respectively. The designed finger is able to grasp objects with different shapes stably.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

One of the key aspects of vehicle-to-grid technology (V2G) is the analysis of uncertainty in electric vehicle user behavior. Correct estimation of the amount of available energy from electric vehicles that are expected to provide ancillary services to the electricity system operator or to secure the end user’s demand is essential to design these services in an appropriate way. Therefore, it is necessary to analyze the probabilities of V2G service performance for different scenarios. This paper presents the author’s approach to determining the values of V2G service provision probabilities using survey data. It was found that estimating these values using simulation and forecasting tools makes sense when the coefficients resulting from survey responses are used as initial data. Thus, the paper also presents the results of the surveys that were conducted. As the results from the simulations show, the values of the probabilities of V2G services are not high, which should induce future operators of V2G services to offer a beneficial product for the customer.

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.

The development of the smart grid requires the distribution switch to not be limited to the original breaking function. More functional requirements lead to more complex switch structures, especially the intelligent processing unit on the secondary side. A technology called primary and secondary integration optimizes the structure of the switch, which greatly increases the intelligence level of the switch, but also has disadvantages. The secondary intelligent unit is arranged close to the primary high-voltage electromagnetic environment, and the distribution switch is prone to failure due to electromagnetic interference. In order to explore the influence of electromagnetic interference on it, a transient electromagnetic interference simulation test platform was built for a 10 kV intelligent distribution switch based on the principle of spherical gap arc discharge, and the interference signal of the intelligent distribution switch was measured; the law of the spatial magnetic field near the electronic transformer is mainly studied in this paper. The shielding effectiveness of the distribution terminal of the switch was analyzed, and the interference of the power line of the sensor merging unit circuit board was calculated. The results show that the electronic transformer may have serious faults under continuous strong transient electromagnetic interference. The electromagnetic transient simulation test system studied in this paper can evaluate the anti strong electromagnetic interference ability of the electronic transformer.

Global electricity markets are undergoing a rapid transformation in their energy mix to meet commitments towards sustainable electric grids. This change in energy mix engenders significant challenges, specifically concerning the management of non-dispatchable energy resources. System and market operators are required to meet power system security and reliability requirements whilst providing electricity at competitive prices. An overview of electricity markets is provided in this paper with a critical appraisal of each market’s ability to manage the large-scale energy mix transition. This paper provides a commentary on the distinct features of electricity market models implemented around the world and highlights the barriers within these market models that are hindering the energy mix transition. Various researchers and policymakers are proposing solutions and market reforms for the smooth transitioning of the energy mix. This paper presents a systematic review of the proposed solutions in the literature and critiques the effectiveness and ease of implementation of the reviewed solutions. Research gaps and future research directions are indicated to promote further exploration towards the effective integration of large-scale renewable energy technologies.

International audience; The development of “portable, low cost and low consumption” gas microsensors is one of the strong needs for embedded portable devices in many fields such as public domain. In this paper, a new approach is presented on making, on the same chip, a network of head-to-tail facing PN junctions in order to miniaturize the sensor network and considerably reduce the required power for heating each cell independently. This paper is about recognizing a device that integrates both sensing and self-heating. This first study aims to evaluate the possibilities of this type of diode network for use as a gas sensor. The first part concerns the description of the technological process that is based on a doped polysilicon wafer in which a thin layer of metal oxide (a gallium-doped zinc oxide in our case) is deposited by RF sputtering. An electrical model will be proposed to explain the operation and advantage of this approach. We will show the two types of tests that have been carried out (static and dynamic) as well as the first encouraging results of these electrical characterizations under variable atmospheres.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

This paper proposes a 5.8 GHz highly efficient rectifier design using harmonic termination for wireless power transfer. The diode used to convert the received RF power to DC is a non-linear device, and a harmonic component is generated, which causes performance degradation. Therefore, in this paper, we designed a band stop filter for harmonic termination and proposed the N-stage harmonic terminated voltage multiplier (N-stage HTVM). The number of stages N can be designed differently to operate with high efficiency at various input powers for the proposed rectifier. In the proposed rectifier circuit, mathematical analysis of output DC voltage, power loss of the diode, and the power conversion efficiency (PCE) were evaluated through voltage/current waveform analysis of the diode. The design method of the filter for terminating harmonics is presented. Furthermore, the change of PCE according to the increase in the number of stages was analyzed using the equivalent model of the proposed circuit and verified through measurement. The maximum PCE of one-stage HTVM was 68% when 18 dBm of input power was applied. The DC output voltage was measured to 11.6 V. When the RF input power was 25 dBm and the load was 1500 Ω, the maximum PCE of the two-stage HTVM was 71% and the maximum DC output voltage was measured as 15.8 V. The measured performance of three-stage HTVM had a PCE of 67% and DC output voltage of 19.8 V when the input power was 30 dBm.

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient.

This paper addresses the challenge of minimizing training time for the control of Heating, Ventilation, and Air-conditioning (HVAC) systems with online Reinforcement Learning (RL). This is done by developing a novel approach to Multi-Agent Reinforcement Learning (MARL) to HVAC systems. In this paper, the environment formed by the HVAC system is formulated as a Markov Game (MG) in a general sum setting. The MARL algorithm is designed in a decentralized structure, where only relevant states are shared between agents, and actions are shared in a sequence, which are sensible from a system’s point of view. The simulation environment is a domestic house located in Denmark and designed to resemble an average house. The heat source in the house is an air-to-water heat pump, and the HVAC system is an Underfloor Heating system (UFH). The house is subjected to weather changes from a data set collected in Copenhagen in 2006, spanning the entire year except for June, July, and August, where heat is not required. It is shown that: (1) When comparing Single Agent Reinforcement Learning (SARL) and MARL, training time can be reduced by 70% for a four temperature-zone UFH system, (2) the agent can learn and generalize over seasons, (3) the cost of heating can be reduced by 19% or the equivalent to 750 kWh of electric energy per year for an average Danish domestic house compared to a traditional control method, and (4) oscillations in the room temperature can be reduced by 40% when comparing the RL control methods with a traditional control method.

Global Positioning System (GPS) is a global navigation satellite system and the most common satellite system used in navigation and tracking devices. The phenomenon of week number rollover happened recently—a year ago—due to a design limitation in the week number variable that counting weeks which causes vast losses. As many fleet management systems depend on GPS raw data, such systems stopped working due to inaccurate data provided by GPS receivers. In this paper, we propose a technical and mathematical analysis for the GPS week number rollover phenomenon and suggest a solution to avoid the resulting damage to other subsystems that depend on the GPS device’s raw data. In addition, this paper seeks to provide precautionary measures to deal with the problem proactively. The Open Systems Interconnection model (OSI) and transport layer level solution that has been suggested depends on a TCP packet reforming tool that re-formats the value of the week number according to a mathematical model based on a timestamp complement. At the level of the database, a solution is also suggested which uses triggers. A hardware-level solution is suggested by applying a timestamp complement over the GPS internal controller. Complete testing is applied for all suggested solutions using actual data provided by Traklink—a leading company in navigation and fleet management solutions. After testing, it is evident that the transport layer level solution was the most effective in terms of speed, efficiency, accuracy, cost, and complexity. Applying a transport layer level complement mathematical model can fix the consequences of GPS week number rollover and provide stability to all subsystems that used GPS data from infected devices.

This paper focuses on the methods that ensure the rotor angle stability of electric power systems, which is most frequently analyzed with small-signal models. Over the past several decades, power system stabilizers (PSSs) for conventional excitation systems were the main tools for improving the small-signal stability of electromechanical oscillatory modes. In the last decade, power oscillation damping (POD) control implemented in photovoltaic (PV) inverters has been considered an alternative to PSSs. As PV generation undergoes massive rollout due to policy directions and renewable energy source integration activities, it could potentially be used as a source of damping, which is crucial for sustaining the rotor angle stability of the remaining in-service synchronous generators. Several studies have already been dedicated to the development of different damping strategies. This paper contributes to the existing research in power system stability by providing a comprehensive review of the effects of PV generation on small-signal stability, as well as the recent evolution of POD control through PV inverters. The features and impacts of the various ways to realize POD controllers are assessed and summarized in this paper. Currently, detailed information and discussions on the practical application of PV inverter PODs are not available. This paper is, thus, intended to initiate a relevant discussion and propose possible implementation approaches concerning the topic under study.

In the process of the continuous development and improvement of modern military systems, military unmanned vehicles play an important role in field reconnaissance and strategic deployment. In this paper, the precise tracking algorithm of a military unmanned vehicle, based on GPS navigation, is studied. Firstly, the optimal preview point is obtained according to the data points of a differential GPS signal. Secondly, the pure tracking algorithm is used to calculate the demand steering angle, and a variable universe fuzzy sliding mode controller is designed to control the lateral motion of the vehicle, which is verified by the joint simulation platform of Simulink and CarSim, under multiple working conditions. Finally, the actual vehicle is verified by using the Autobox platform. The results show that the lateral motion control of path tracking designed in this paper can achieve an accurate and effective control effect, and has real-time performance for engineering applications.

In time attack races, aerodynamics plays a vital role in achieving short track times. These races are characterized by frequent braking and acceleration supported by aerodynamic downforce. Usually, typical cars are modified for these races by amateurs. Adjusting the aerodynamic solutions to work with bodies developed for other flow conditions is difficult. This paper presents the results of a numerical analysis of the effects of installing a straight wing in front of or above the body on the modified vehicle system’s aerodynamic characteristics, particularly on the front wheels’ aerodynamic downforce values. The paper presents the methodology and results of calculations of the aerodynamic characteristics of a car with an additional wing placed in various positions in relation to the body. The numerical results are presented (Cd, Cl, Cm, Clf, Clr), as well as exemplary pressure distributions, pathlines, and visualizations of vortex structures. Strong interactions between the wing operation and body streamline structure are shown. An interesting and unexpected result of the analysis is that the possibility of obtaining aerodynamic downforce of the front wheels is identified, without an increase in aerodynamic drag, by means of a wing placed in a proper position in front of the body. A successful attempt to balance the additional downforce coming from the front wing on the front axle is made using a larger spoiler. However, for large angles of attack, periodically unsteady flow is captured with frequency oscillations of ca. 6–12 Hz at a car speed of 40 m/s, which may interfere with the sports car’s natural suspension frequency.

Due to the constant trend of building wind turbine sets and photovoltaic cells, the so-called renewable energy sources, it is important to develop a mathematical model that will allow us to analyse the economic viability of their operation. By using this model, multivariate calculations should be carried out and developed in the form of universal nomograms that will allow us to estimate this viability almost on an immediate basis. In this paper, such a model for the unit cost of electricity production from renewable energy sources is presented. It is based on a continuous-time Net Present Value notation. The multi-variant calculations performed with it take into account different subsidy levels and operating time of the renewable energy sources. The paper presents values of unit costs of electricity production from renewable energy sources and values of maximum subsidies for this production. It turns out that subsidies are necessary for renewable energy sources to exist on the energy market. An additional disadvantage is the fact that renewable energy sources are not able to provide a stable electricity supply throughout the year.

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.

Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.