A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of ou... [more]

Wireless power transfer using a magnetic field through inductive coupling is steadily entering the market in a broad range of applications. However, for certain applications, capacitive wireless power transfer using electric coupling might be preferable. In order to obtain a maximum power transfer efficiency, an optimal compensation network must be designed at the input and output ports of the capacitive wireless link. In this work, the conjugate image theory is applied to determine this optimal network as a function of the characteristics of the capacitive wireless link, as well for the series as for the parallel topology. The results are compared with the inductive power transfer system. Introduction of a new concept, the coupling function, enables the description of the compensation network of both an inductive and a capacitive system in two elegant equations, valid for the series and the parallel topology. This approach allows better understanding of the fundamentals of the wireles... [more]

Accurate electric power demand forecasting plays a key role in electricity markets and power systems. The electric power demand is usually a non-linear problem due to various unknown reasons, which make it difficult to get accurate prediction by traditional methods. The purpose of this paper is to propose a novel hybrid forecasting method for managing and scheduling the electricity power. EEMD-SCGRNN-PSVR, the proposed new method, combines ensemble empirical mode decomposition (EEMD), seasonal adjustment (S), cross validation (C), general regression neural network (GRNN) and support vector regression machine optimized by the particle swarm optimization algorithm (PSVR). The main idea of EEMD-SCGRNN-PSVR is respectively to forecast waveform and trend component that hidden in demand series to substitute directly forecasting original electric demand. EEMD-SCGRNN-PSVR is used to predict the one week ahead half-hour’s electricity demand in two data sets (New South Wales (NSW) and Victorian... [more]

In view of a possible use as anode materials in acidic direct alcohol fuel cells, the electro-catalytic activity of Pt-Ni and Pt-M-Ni (M = Ru, Sn) catalysts for methanol and ethanol oxidation has been widely investigated. An overview of literature data regarding the effect of the addition of Ni to Pt and Pt-M on the methanol and ethanol oxidation activity in acid environment of the resulting binary and ternary Ni-containing Pt-based catalysts is presented, highlighting the effect of alloyed and non-alloyed nickel on the catalytic activity of these materials.

Low-voltage direct current (LVDC) distribution systems have been evolving into interesting ways of integrating distributed energy resources (DERs) and power electronics loads to local distribution networks. In LVDC distribution systems, voltage regulation is one of the most important issues, whereas AC systems have concerns such as frequency, power factor, reactive power, harmonic distortion and so on. This paper focuses on a voltage control method for a LVDC distribution system based on the concept of multi-agent system (MAS), which can deploy intelligence and decision-making abilities to local areas. This paper proposes a distributed power flow analysis method using local information refined by local agents and communication between agents based on MAS. This paper also proposes a voltage control method by coordinating the main AC/DC converter and multiple DERs. By using the proposed method, we can effectively maintain the line voltages in a pre-defined normal range. The performance o... [more]

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of... [more]

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM) to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also ve... [more]

Numerous analytical studies for power augmentation systems can be found in the literature with the goal to improve the performance of wind turbines by increasing the energy density of the air at the rotor. All methods to date are only concerned with the effects of a diffuser as the power augmentation, and this work extends the semi-empirical shrouded wind turbine model introduced first by Foreman to incorporate a converging-diverging nozzle into the system. The analysis is based on assumptions and approximations of the conservation laws to calculate optimal power coefficients and power extraction, as well as augmentation ratios. It is revealed that the power enhancement is proportional to the mass stream rise produced by the nozzle diffuser-augmented wind turbine (NDAWT). Such mass flow rise can only be accomplished through two essential principles: the increase in the area ratios and/or by reducing the negative back pressure at the exit. The thrust coefficient for optimal power produc... [more]

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by... [more]

The transition of our current energy system from a fossil-based system to a system based on renewables is likely to be one of the most complex and long-term societal transitions in history. The need for a fundamental system transformation raises the question of how to measure the continuing progress and the resilience of this process over time. This paper aims at developing the conceptualization and operationalization of resilience for energy systems in transition with regard to both social and technical aspects. Based on the resilience concept in social-ecological systems literature, we propose to conceptualize resilience for energy systems building on two core attributes of resilience, namely diversity and connectivity. We present an indicator set to operationalize these key attributes in social and technical systems using: (i) definitions and measurements for three fundamental diversity properties—variety, balance and disparity—and (ii) basic connectivity properties from the social... [more]

Bidirectional power converters for electric vehicles (EVs) have received much attention recently, due to either grid-supporting requirements or emergent power supplies. This paper proposes a hybrid modulation of the three-phase dual-active bridge (3ΦDAB) converter for EV charging systems. The designed hybrid modulation allows the converter to switch its modulation between phase-shifted and trapezoidal modes to increase the conversion efficiency, even under light-load conditions. The mode transition is realized in a real-time manner according to the charging or discharging current. The operation principle of the converter is analyzed in different modes and thus design considerations of the modulation are derived. A lab-scaled prototype circuit with a 48V/20Ah LiFePO₄ battery is established to validate the feasibility and effectiveness.

Developing the so-called grey box or hybrid models of limited complexity for process systems is the cornerstone in advanced control and real-time optimization routines. These models must be based on fundamental principles and customized with sub-models obtained from process experimental data. This allows the engineer to transfer the available process knowledge into a model. However, there is still a lack of a flexible but systematic methodology for grey-box modeling which ensures certain coherence of the experimental sub-models with the process physics. This paper proposes such a methodology based in data reconciliation (DR) and polynomial constrained regression. A nonlinear optimization of limited complexity is to be solved in the DR stage, whereas the proposed constrained regression is based in sum-of-squares (SOS) convex programming. It is shown how several desirable features on the polynomial regressors can be naturally enforced in this optimization framework. The goodnesses of the... [more]

In order to explore the catastrophic evolution process for karst cave water inrush in large buried depth and high water pressure tunnels, a model test system was developed, and a similar fluid−solid coupled material was found. A model of the catastrophic evolution of water inrush was developed based on the Xiema Tunnel, and the experimental section was simulated using the finite element method. By analyzing the interaction between groundwater and the surrounding rocks during tunnel excavation, the law of occurrence of water inrush disaster was summarized. The water inrush process of a karst cave containing high-pressure water was divided into three stages: the production of a water flowing fracture, the expansion of the water flowing fracture, and the connection of the water flowing fracture. The main cause of water inrush in karst caves is the penetration and weakening of high-pressure water on the surrounding rock. This effect is becoming more and more obvious as tunnel excavation pr... [more]

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the exp... [more]

This study investigates the process of how the original equipment manufacturers (OEMs) in automobile and consumer electronics industries design their supply networks. In contrast to the sociological viewpoint, which regards the emergence of networks as a social and psychological phenomenon occurring among non-predetermined individuals, this paper attempts to provide a strategic supply network perspective that views the supply network as a strategic choice made by an OEM. Anchored in the multiplex investigation of supply network architectures, this study looks into the following specific questions: (1) Are an OEM’s strategic intent choices associated with supply network architecture and (2) If so, what differential effects do those strategic intents have on the architectural properties of the supply network? Further field investigations were conducted to provide deeper insights into the quantitative and qualitative findings from statistical analyses.

Mixing phenomena in metallurgical steel ladles by bottom gas injection involves three phases namely, liquid molten steel, liquid slag and gaseous argon. In order to numerically solve this three-phase fluid flow system, a new approach is proposed which considers the physical nature of the gas being a dispersed phase in the liquid, while the two liquids namely, molten steel and slag are continuous phases initially separated by a sharp interface. The model was developed with the combination of two algorithms namely, IPSA (inter phase slip algorithm) where the gas bubbles are given a Eulerian approach since are considered as an interpenetrating phase in the two liquids and VOF (volume of fluid) in which the liquid is divided into two separate liquids but depending on the physical properties of each liquid they are assigned a mass fraction of each liquid. This implies that both the liquid phases (steel and slag) and the gas phase (argon) were solved for the mass balance. The Navier−Stokes c... [more]

Global Sensitivity Analysis (GSA) is a technique that numerically evaluates the significance of model parameters with the aim of reducing the number of parameters that need to be estimated accurately from experimental data. In the work presented herein, we explore different methods and criteria in the sensitivity analysis of a recently developed mathematical model to describe Chinese hamster ovary (CHO) cell metabolism in order to establish a strategic, transferable framework for parameterizing mechanistic cell culture models. For that reason, several types of GSA employing different sampling methods (Sobol’, Pseudo-random and Scrambled-Sobol’), parameter deviations (10%, 30% and 50%) and sensitivity index significance thresholds (0.05, 0.1 and 0.2) were examined. The results were evaluated according to the goodness of fit between the simulation results and experimental data from fed-batch CHO cell cultures. Then, the predictive capability of the model was tested against four different... [more]

Carbonaceous adsorbents with both high sulfur capacity and easy regeneration are required for flue gas desulfurization. A hierarchical structure is desirable for SO2 removal, since the micropores are beneficial for SO2 adsorption, while the mesopore networks facilitate gas diffusion and end-product H2SO4 storage. Herein, an ordered hierarchical porous carbon was synthesized via a soft-template method and subsequent activation, used in SO2 removal, and compared with coal-based activated carbon, which also had a hierarchical pore configuration. The more detailed, abundant micropores created in CO2 activation, especially the ultramicropores (d < 0.7 nm), are essential in enhancing the SO2 adsorption and the reserves rather than the pore patterns. While O2 and H2O participate in the reaction, the hierarchical porous carbon with ordered mesopores greatly improves SO2 removal dynamics and sulfur capacity, as this interconnecting pore pattern facilitates H2SO4 transport from micropores to... [more]

Intelligent data analytics-based cloud computing is a leading trend for managing a large-scale network in contemporary manufacturing environments. The data and information are shared using the cloud environments and valuable knowledge is driven using the embedded intelligence analytics. This research applied this trend to the control of a key supplier’s real-time production planning for solving joint production goals with downstream producers. As a key supplier has several downstream producers in general, several uncertainties are embedded on the supply chain network such as the quality issue in the supplier and the occurrence of unexpected orders from the downstream industries. While the control of a supply plan is difficult considering these dynamics in traditional frameworks, the proposed framework detects the dynamic changes accurately using the constructed cloud system. Moreover, the real-time control considering uncertain scenarios as well as the extracted knowledge is achieved u... [more]

Deep eutectic solvents (DESs) are increasingly receiving interest as a new type of green and sustainable alternative to hazardous organic solvents. In this work, a novel DES based on levulinic acid (La) and 1,4-butanediol (Buta) as an extraction media was developed for extracting the bioactive alkaloid rutaecarpine from the unripe fruits of Tetradium ruticarpum. 24 different DESs consisting of choline chloride, betaine, sugar alcohols, organic acids, amides, and sugars were prepared and tailored to test their extraction efficiency. After initial screening, a hydrophilic DES composed of La and Buta with 1:0.5 molar ratio containing 25% water was tailored for the highest extraction efficiency, followed by the optimizations of molar ratio and water content. The interaction between the molecules of La-Buta DES was investigated by nuclear magnetic resonance spectroscopy in order to confirm its deep eutectic supermolecular structure feature. The extraction conditions were optimized by single... [more]

The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB... [more]

The continuous increase in the number of stringent exhaust emission legislations of marine Diesel engines had led to a decrease in NOx emissions at the required level. Selective catalyst reduction (SCR) is the most prominent and mature technology used to reduce NOx emissions. However, to obtain maximum NOx removal with minimum ammonia slip remains a challenge. Therefore, new mixers are designed in order to obtain the maximum SCR efficiency. This paper reports performance parameters such as uniformity of velocity, ammonia uniformity distribution, and temperature distribution. Also, a numerical model is developed to investigate the interaction of urea droplet with exhaust gas and its effects by using line (LM) and swirl (SM) type mixers alone and in combination (LSM). The urea droplet residence time and its interaction in straight pipe are also investigated. Model calculations proved the improvement in velocity uniformity, distribution of ammonia uniformity, and temperature distribution... [more]

The objective of this research was to explore the changes of the pore structure and surface properties of nitric-modified lignite and base the adsorption performance on physical and chemical adsorbent characteristics. To systematically evaluate pore structure and surface chemistry effects, several lignite samples were treated with different concentrations of nitric acid in order to get different pore structure and surface chemistry adsorbent levels. A common heavy metal ion contaminant in water, Pb2+, served as an adsorbate probe to demonstrate the change of modified lignite adsorption properties. The pore structure and surface properties of lignite samples before and after modification were characterized by static nitrogen adsorption, X-ray diffraction, Scanning electron microscope, Fourier transform infrared spectroscopy, zeta potential, and X-ray photoelectron spectroscopy. The experimental results showed that nitric acid modification can increase the ability of lignite to adsorb Pb... [more]

Intensified and accelerated development processes are being demanded by the market, as innovative biopharmaceuticals such as virus-like particles, exosomes, cell and gene therapy, as well as recombinant proteins and peptides will possess no available platform approach. Therefore, methods that are able to accelerate this development are preferred. Especially, physicochemical rigorous process models, based on all relevant effects of fluid dynamics, phase equilibrium, and mass transfer, can be predictive, if the model is verified and distinctly quantitatively validated. In this approach, a macroscopic kinetic model based on Monod kinetics for mammalian cell cultivation is developed and verified according to a general valid model validation workflow. The macroscopic model is verified and validated on the basis of four decision criteria (plausibility, sensitivity, accuracy and precision as well as equality). The process model workflow is subjected to a case study, comprising a Chinese hamst... [more]

Aiming to further improve the dust suppression performance of the dust suppressant, the present study independently develops a new type of biodegradable environmentally-friendly dust suppressant. Specifically, the naturally occurring biodegradable soybean protein isolate (SPI) is selected as the main material, which is subject to an anionic surfactant, i.e., sodium dodecyl sulfonate (SDS) for modification with the presence of additives including carboxymethylcellulose sodium and methanesiliconic acid sodium. As a result, the SDS-SPI cementing dust suppressant is produced. The present study experimentally tests solutions with eight different dust suppressant concentrations under the same experimental condition, so as to evaluate their dust suppression performances. Key metrics considered include water retention capability, cementing power and dust suppression efficiency. The optimal concentration of dust suppressant solution is determined by collectively comparing these metrics. The exp... [more]