Petroleum resources are finite and, therefore, search for their alternative non-petroleum fuels for internal combustion engines is continuing all over the world. Moreover gases emitted by petroleum ...fuel driven vehicles have an adverse effect on the environment and human health. There is universal acceptance of the need to reduce such emissions. Towards this, scientists have proposed various solutions for diesel engines, one of which is the use of gaseous fuels as a supplement for liquid diesel fuel. These engines, which use conventional diesel fuel and gaseous fuel, are referred to as ‘dual-fuel engines’. Natural gas and bio-derived gas appear more attractive alternative fuels for dual-fuel engines in view of their friendly environmental nature. In the gas-fumigated dual-fuel engine, the primary fuel is mixed outside the cylinder before it is inducted into the cylinder. A pilot quantity of liquid fuel is injected towards the end of the compression stroke to initiate combustion. When considering a gaseous fuel for use in existing diesel engines, a number of issues which include, the effects of engine operating and design parameters, and type of gaseous fuel, on the performance of the dual-fuel engines, are important. This paper reviews the research on above issues carried out by various scientists in different diesel engines. This paper touches upon performance, combustion and emission characteristics of dual-fuel engines which use natural gas, biogas, producer gas, methane, liquefied petroleum gas, propane, etc. as gaseous fuel. It reveals that ‘dual-fuel concept’ is a promising technique for controlling both NO
x
and soot emissions even on existing diesel engine. But, HC, CO emissions and ‘bsfc’ are higher for part load gas diesel engine operations. Thermal efficiency of dual-fuel engines improve either with increased engine speed, or with advanced injection timings, or with increased amount of pilot fuel. The ignition characteristics of the gaseous fuels need more research for a long-term use in a dual-fuel engine. It is found that, the selection of engine operating and design parameters play a vital role in minimizing the performance divergences between an existing diesel engine and a ‘gas diesel engine’.
In this research, we have covalently functionalized graphene oxide (GO) with hydrophilic and biocompatible Pluronic F38 (F38), Tween 80 (T80) and maltodextrin (MD) for loading and delivery of a ...poorly water soluble antioxidant and anticancer drug, ellagic acid (EA). The functionalized GO showed a good aqueous solubility and biocompatibility. This is the first time that the EA was loaded onto GO-F38, GO-T80 and GO-MD through π-π interactions, yielding a loading capacity of 1 g, 1.22 g and 1.14 g of EA per gram of GO-F38, GO-T80, and GO-MD respectively. Their capabilities to kill human breast carcinoma cells (MCF7) and human colon adenocarcinoma cells (HT29) were then investigated. The release of EA from these nanocarriers was studied in water (neutral pH) and buffer solutions of pH 4 and 10 at 37 ° C. The GO-F38, GO-T80 and GO-MD released ˜ 36-38% drug within 3 days at pH 10. The cytotoxicity of EA loaded onto the functionalized GO was higher than that of free EA dissolved in DMSO. The DPPH assay was used to study the antioxidant activity, and the very similar antioxidant activities were obtained for three EA-loaded nanocarriers and the free EA, indicating that loading of EA onto the functionalized GO did not hamper its antioxidant activity. Therefore, all three functionalized GOs are suitable nanocarriers for drug delivery because of their non-toxicity and high drug loading capacity.
This letter proposes a novel deep learning-based multi-task approach for non-intrusive monitoring of home appliances-the first of its kind-where a network can simultaneously estimate the states and ...disaggregate energies of multiple appliances. An attention-powered encoder-decoder network, comprising a convolutional layer and a long short-term memory, is deployed for the above tasks. Test results from two real-world datasets demonstrate the approach's feasibility, showcasing superior performance and reduced memory requirements.
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• Quercetin nanoparticles fabricated by evaporative precipitation of nanosuspension. • Smallest quercetin particles of diameter 220 nm were obtained. • Complexes with β-cyclodextrin, ...solid dispersions with PVP & pluronic F127 prepared. • Dissolution of nanoparticles, complexes and solid dispersions ⋙ raw quercetin. • Diffusion found to be the main release mechanism using Korsemeyer–Peppas model.
The main aim of this study was to enhance the dissolution rate of a poorly water-soluble antioxidant drug, quercetin, by fabricating its nanoparticles, complexes and solid dispersions using evaporative precipitation of nanosuspension (EPN). We studied the influence of the type of antisolvent, drug concentration and solvent to antisolvent ratio on the quercetin particles formed during EPN. With water as antisolvent, the particles were big, irregular and flake type but with benzene or hexane as antisolvent, the particles were smaller and needle type. Smallest particles of 220
nm diameter were achieved with hexane as antisolvent, lowest drug concentration and highest solvent to antisolvent ratio. The relative dissolution values showed that the dissolution rate of the EPN prepared quercetin nanoparticles was much higher than that of the raw drug. Quercetin formed inclusion complexes with β-cyclodextrin, and solid dispersions with polyvinylpyrrolidone and pluronic F127, where quercetin was present in an amorphous form and/or was dispersed at a molecular level. The dissolution rate of quercetin in its complexes and solid dispersions improved significantly from the raw quercetin as indicated by the percent dissolution efficiency. It was interesting to note that at lower carrier concentration, the solid dispersions of quercetin with polyvinylpyrrolidone and pluronic F127 presented better dissolution than its complex with β-cyclodextrin but at higher carrier concentration, there was no significant difference in the dissolution behavior of the three formulations. Using Korsmeyer–Peppas model, diffusion was found to be the main release mechanism.
Monitoring individual appliances’ operating state and energy consumption in a building enables significant energy-saving opportunities. These days, smart meters perform this task nonintrusively using ...sophisticated signal processing, machine-learning, and/or deep-learning (DL) approaches. To this end, this article proposes a novel multitask DL model that uses readily available low-frequency energy data from the smart meter for simultaneous appliance state detection (SD) and energy disaggregation (ED). The model creatively adopts and customizes the famous transformer model from the field of language modeling for the above task. Furthermore, the model output is produced as a mixture of probability density functions to handle uncertainties. The model performance is evaluated using the publicly available REFIT and UKDALE datasets. The test results indicate the proposed model’s superiority, generalizability, and transferability compared to other state-of-the-art models.
► Simultaneous optimization of cost and reliability is done. ► The approach provides a set of Pareto solutions. ► The optimal feeder routes and branch conductor sizes are determined. ► Performance ...comparison with MOEA-based approaches shows better results.
This paper presents a novel dynamic programming approach for multi-objective planning of electrical distribution systems. In this planning, the optimal feeder routes and branch conductor sizes of a distribution system are determined by simultaneous optimization of cost and reliability. The multiple planning objectives are minimization of: (i) installation and operational cost, and (ii) interruption cost. The first objective function consists of the installation cost of new feeder branches and substations, maintenance cost of the existing and new feeder branches, and the cost of energy losses. The second objective function measures the reliability of the distribution network in terms of the associated interruption costs for all the branches, which includes the cost of non-delivered energy, cost of repair, and the customer damage cost due to interruptions. A dynamic programming based planning algorithm for optimization of the feeder routes and branch conductor sizes is proposed. A set of Pareto solutions is obtained using a weighted aggregation of the two objectives with different weight settings. The proposed approach is evaluated on 21-, 54-, and 100-node distribution systems. The simulation test results are analyzed with various case studies and are compared with those of two existing planning approaches based on multi-objective evolutionary algorithm.
This paper presents a multi-objective planning approach for electrical distribution systems under uncertainty in load demand incorporating distributed generation (DG). Both radial and meshed systems ...are considered. The overall influence of load demand uncertainty on planned networks is investigated in detail. Uncertainty in load demand is possibilistically modeled using a fuzzy triangular number. The two objectives in system planning are: (i) minimization of total installation and operational costs, and (ii) minimization of the risk factor. The risk factor is a function of the contingency load-loss index (CLLI), which measures load loss under contingencies, and the degree of network constraints violations. CLLI minimization improves network reliability. The network variables optimized are: (i) the network structure type (radial or meshed), (ii) the number of feeders and their routes, and (iii) the number and location of sectionalizing switches. The optimization tool is a multi-objective particle swarm optimization (MOPSO) variant that uses heuristic selection and assignment of leaders or guides for efficient identification of non-dominated solutions. The optimal number, location, and size of the DG units are determined in another planning stage. Performance comparisons between the planning approaches with possibilistic and deterministic load models highlight the relative merits and demerits. The advantages of networks obtained using the proposed planning approach in the context of DG integration are described. The proposed planning approach is validated using three typical distribution systems.
The aim of the current study is to simulate the long-term spatiotemporal soil moisture variation in the lower Mahanadi basin using the Soil and Water Assessment Tool (SWAT) model. The model was ...calibrated and validated using stream discharge data for the periods from 2005 to 2012 and 2013 to 2017, respectively. The coefficient of determination (
R
2
) and Nash–Sutcliffe-Efficiency (NSE) values were 0.81 and 0.79 during calibration, and 0.78 and 0.74 during validation. The spatiotemporal annual and seasonal soil moisture content maps were prepared sub-basin-wise for the study period (2005–2017). The depth of the simulated soil moisture content varies with the rainfall, land use, and soil types of the lower Mahanadi basin. The upper region of the lower Mahanadi basin shows a higher simulated soil moisture content compared to the downstream region. The amounts of average annual rainfall in agricultural land, barren land, deciduous forest, and wasteland were 416.25, 439.06, 403.04, and 409.90 mm, respectively, which correspond to 118.18, 111.48, 126.78, and 121.50 mm/m of soil moisture content. The seasonal soil moisture maps showed that after harvesting the
kharif
rice crop, a sufficient amount of soil moisture was available during the post-monsoon season. Therefore, short-duration pulses and oilseed crops are recommended in this region to utilize the residual simulated soil moisture content, which could bring unutilized areas into cultivation and enhance farmer’s income. Further, the simulated soil moisture content was compared with the satellite-derived SMAP-based soil moisture content, and a reasonably good agreement was found between the observed and simulated soil moisture content. The overall result showed that the SWAT model can reasonably simulate the spatiotemporal variation of soil moisture content.
The modeling of driver behavior plays an essential role in developing Advanced Driver Assistance Systems (ADAS) to support the driver in various complex driving scenarios. The behavior estimation of ...surrounding vehicles is crucial for an autonomous vehicle to safely navigate through an unsignalized intersection. This work proposes a novel kernelized convolutional transformer network (KCTN) with multi-head attention (MHA) mechanism to estimate driver behavior at a challenging unsignalized three-way roundabout. More emphasis has been placed on creating convolution in non-linear space by introducing a kervolution operation into the proposed network. It generalizes convolution, improves model capacity, and captures higher-order feature interactions by using Gaussian kernel function. The proposed model is validated using the real-world ACFR dataset, where it outperforms current state-of-the-art in terms of behavior prediction accuracy and provides a significant lead time before potential conflict situations.
•Kervolution is combined with transformer to predict driving behavior at roundabouts.•The kernel trick generalizes convolution in non-linear space to increase accuracy.•Transformer network’s utility is illustrated to deal sequence to point problems.•The proposed model achieves higher accuracy compared to RNN-based sequential models.•The framework enables for the modeling of dynamic and complex intentions.
Present energy need heavily relies on the conventional sources. But the limited availability and steady increase in the price of conventional sources has shifted the focus toward renewable sources of ...energy. Of the available alternative sources of energy, wind energy is considered to be one of the proven technologies. With a competitive cost for electricity generation, wind energy conversion system (WECS) is nowadays deployed for meeting both grid-connected and stand-alone load demands. However, wind flow by nature is intermittent. In order to ensure continuous supply of power suitable storage technology is used as backup. In this paper, the sustainability of a 4-kW hybrid of wind and battery system is investigated for meeting the requirements of a 3-kW stand-alone dc load representing a base telecom station. A charge controller for battery bank based on turbine maximum power point tracking and battery state of charge is developed to ensure controlled charging and discharging of battery. The mechanical safety of the WECS is assured by means of pitch control technique. Both the control schemes are integrated and the efficacy is validated by testing it with various load and wind profiles in MATLAB/SIMULNIK.
