Fabrication and full characterization of state-of-the-art quantum dot luminescent solar concentrators

The fabrication and full characterization of luminescent solar concentrators (LSCs) comprising CdSe core/multishell quantum dots (QDs) is reported. TEM analysis shows that the QDs are well dispersed in the acrylic medium while maintaining a high quantum yield of 45%, resulting in highly transparent and luminescent polymer plates. A detailed optical analysis of the QD-LSCs including absorption, emission, and time-resolved fluorescence measurements is presented. Both silicon and GaAs solar cells attached to the side of the QD-LSCs are used to measure the external quantum efficiency and power conversion efficiency (2.8%) of the devices. Stability tests show only a minor decrease of 4% in photocurrent upon an equivalent of three months outdoor illumination. The optical data are used as input for a ray-trace model that is shown to describe the properties of the QD-LSCs well. The model was then used to extrapolate the properties of the small test devices to predict the power conversion efficiency of a 50×50 cm² module with a variety of different solar cells. The work described here gives a detailed insight into the promise of QD-based LSCs.     

Self-sacrificing template synthesis of CdS quantum dots/Cd-Hap composite photocatalysts for excellent H2 production under visible light

Well dispersive CdS quantum dots (QDs) were successfully in-situ grown on cadmium hydroxyapatite (Cd₅(PO₄)₃OH, Cd-Hap) assembled rods through a self-sacrificing hydrothermal method. No any nocuous organic ligands were used in such self-sacrificing route, allowing for a green approach to prepare CdS QDs with clean surfaces and enough active sites. The deposition of CdS QDs onto Cd-Hap surfaces led to a dramatically enhanced performance in H₂ production under visible light irradiation as compared to bulk CdS nanoparticles. The optimal CdS QDs/Cd-Hap composite displayed a H₂ evolution rate of 14.1 μmol h^?1 without using any noble metal cocatalyst, which was about 4.2 times higher than that of pristine CdS. The apparent quantum efficiency for CdS QDs/Cd-Hap composite was up to 18%. It was also found that CdS QDs/Cd-Hap composite can continuously generate H₂ from water in the presence of electron donors for more than 125 h. The enhanced photocatalytic performance of CdS QDs/Cd-Hap composites could be attributed to the high charge separation efficiency resulting from the efficient capture of photoinduced electrons by oxygen vacancies in Cd-Hap rods and the quantum confinement effect of CdS QDs with strong redox capacity as well as the increased active sites.     

Enhanced H2 evolution and the interfacial electron transfer mechanism of titanate nanotube sensitized with CdS quantum dots and graphene quantum dots

Synergistic the modulation of photon absorption capability and interfacial charge transfer of the photocatalyst are highly required for developing high-performance heterojunction photocatalysts. The ternary CdS-graphene quantum dots-titanate nanotubes (CdS-GQDs-TNTs) nanocomposite have been prepared by an in situ growth method. The physicochemical characterization reveals that the GQDs are firmly decorated on both inner and outer surface of TNT through the formation of Ti–O–C chemical bonding, and CdS QDs are loaded on the outer surface of TNTs through strong interfacial interaction. The intimate integrated CdS-GQDs-TNTs nanocomposite exhibits much superior photocatalytic performance toward H₂ production compared with binary GQDs-TNTs and pure TNTs photocatalyst, which can be attributed to the combined interaction of the stronger visible light harvesting, the longer lifetime of photogenerated electron−hole pairs, faster interfacial charge transfer rate, fast and long-distance electron transport pass. The interfacial charge transfer mechanism of CdS-GQDs-TNTs ternary composite are proposed based on photoelectrochemical measurements.     

A novel computational approach to study proton transfer in perfluorosulfonic acid membranes

Density functional theory (DFT) calculations were done to obtain the energies of two perfluorosulfonic acid membranes at low humidity conditions. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the energies derived by the DFT method. The ANN method substantially does speed up the ab initio electronic structure calculations and has superior accuracy to mimic the results of such calculations. The designed ANNs for modeling the total and binding energies had high performance since the computed R² values were 0.99998 and 0.990, and the calculated root mean squared error (RMSE) values were 0.612173 Ha and 0.084901 Ha in predicting the total and binding energies, respectively. Statistical analysis of binding energies per water molecule using analysis of means (ANOM) and analysis of variance (ANOVA) methods showed that the hydration level has significant influence on the proton transfer in the perfluorosulfonic acid membranes. ANOM and ANOVA methods were also employed to determine the quantitative effect of other parameters (i.e., temperature and total charge of the system) as well as the combined effect of these parameters. The ease of the proton transfer was also assessed with the aid of the obtained potential energy surfaces.     

A new technique for solving the multi-objective optimization problem using hybrid approach

Energy efficiency, which consists of using less energy or improving the level of service to energy consumers, refers to an effective way to provide overall energy. But its increasing pressure on the energy sector to control greenhouse gases and to reduce CO₂ emissions forced the power system operators to consider the emission problem as a consequential matter besides the economic problems. The economic power dispatch problem has, therefore, become a multi-objective optimization problem. Fuel cost, pollutant emissions, and system loss should be minimized simultaneously while satisfying certain system constraints. To achieve a good design with different solutions in a multi-objective optimization problem, fuel cost and pollutant emissions are converted into single optimization problem by introducing penalty factor. Now the power dispatch is formulated into a bi-objective optimization problem, two objectives with two algorithms, firefly algorithm for optimization the fuel cost, pollutant emissions and the real genetic algorithm for minimization of the transmission losses. In this paper the new approach (firefly algorithm-real genetic algorithm, FFA-RGA) has been applied to the standard IEEE 30-bus 6-generator. The effectiveness of the proposed approach is demonstrated by comparing its performance with other evolutionary multi-objective optimization algorithms. Simulation results show the validity and feasibility of the proposed method.     

A new memetic algorithm approach for the price based unit commitment problem

Unit commitment (UC) is a very important optimization task, which plays a major role in the daily operation planning of electric power systems that is why UC is a core research topic attracting a lot of research efforts. An innovative method based on an advanced memetic algorithm (MA) for the solution of price based unit commitment (PBUC) problem is proposed. The main contributions of this paper are: (i) an innovative two-level tournament selection, (ii) a new multiple window crossover, (iii) a novel window in window mutation operator, (iv) an innovative local search scheme called elite mutation, (v) new population initialization algorithm that is specific to PBUC problem, and (vi) new PBUC test systems including ramp up and ramp down constraints so as to provide new PBUC benchmarks for future research. The innovative two-level tournament selection mechanism contributes to the reduction of the required CPU time. The method has been applied to systems of up to 110 units and the results show that the proposed memetic algorithm is superior to other methods since it finds the optimal solution with a high success rate and within a reasonable execution time.     

A review of physical modelling and numerical simulation of long-term geological storage of CO2

Numerical simulations are essential to the understanding of the long-term geological storage of CO₂. Physical modelling of geological storage of CO₂ has been based on Darcy’s law, together with the equations of conservation of mass and energy. Modelling and simulations can be used to predict where CO₂ is likely to flow, to interpret the volume and spatial distribution of CO₂ under storage conditions, and to optimise injection operations. The state of the art of physical modelling and numerical simulation of CO₂ dispersion is briefly reviewed in this paper, which calls for more accurate and more efficient modelling approaches. A systematic evaluation of the numerical methods used and a comparison between the streamline based methods and the grid based methods would be valuable. Multi-scale modelling may prove to be of great value in predicting the long-term geological storage of CO₂, while highly accurate numerical methods such as high-order schemes may be employed in numerical simulations of CO₂ dispersion for local transport calculations.     

A new optimization approach to energy network modeling: anthropogenic CO2 capture coupled with enhanced oil recovery

To meet next generation energy needs such as wind‐ and solar‐generated electricity, enhanced oil recovery (EOR), CO₂ capture and storage (CCS), and biofuels, the US will have to construct tens to hundreds of thousands of kilometers of new transmission lines and pipelines. Energy network models are central to optimizing these energy resources, including how best to produce, transport, and deliver energy‐related products such as oil, natural gas, electricity, and CO₂. Consequently, understanding how to model new transmission lines and pipelines is central to this process. However, current energy models use simplifying assumptions for deploying pipelines and transmission lines, leading to the design of more costly and inefficient energy networks. In this paper, we introduce a two‐stage optimization approach for modeling CCS infrastructure. We show how CO₂ pipelines with discrete capacities can be ‘linearized’ without loss of information and accuracy, therefore allowing necessarily complex energy models to be solved. We demonstrate the new approach by designing a CCS network that collects large volumes of anthropogenic CO₂ (up to 45 million tonnes of CO₂ per year) from ethylene production facilities and delivers the CO₂ to depleted oil fields to stimulate recovery through EOR. Utilization of anthropogenic CO₂ has great potential to jumpstart commercial‐scale CCS while simultaneously reducing the carbon footprint of domestic oil production. Model outputs illustrate the engineering challenge and spatial extent of CCS infrastructure, as well as the costs (or profits) of deploying CCS technology. We show that the new linearized approach is able to offer insights that other network approaches cannot reveal and how the approach can change how we develop future energy systems including transporting massive volumes of shale gas and biofuels as well as electricity transmission for wind and solar energy. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Application of distributed combustion technique to hydrogen-rich coal gases: A numerical investigation

Distributed combustion has been a promising combustion technique, for enabling a more uniform thermal field, resulting in ultra-low pollutant emissions, reduced combustion noise, and enhanced combustion efficiency. This work examines combustion of hydrogen-rich coal gases derived from Turkish coal under distributed combustion conditions. Focus here is on obtaining a broadened flame and reducing pollutant emissions. Numerical modelling was carried out using a commercial code in order to predict the thermal field and pollutant emissions of the hydrogen-rich coal gases under distributed combustion conditions. A gas mixture (90% N₂ and 10% CO₂) was utilized to simulate controlled entrainment of hot reactive product gases into the fresh mixture prior to ignition in order to seek distributed combustion. The results showed that distributed combustion provided far more uniform thermal field that resulted in greatly reduced NOx emissions. The results also showed that the temperature difference between the maximum and exit temperature was reduced to approximately 200 K under distributed conditions. In addition, the NOx pollutant emissions predicted for each gas were reduced to near zero levels under high intensity distributed combustion conditions supporting the available experimental data. It has been concluded that enhanced thermal field uniformity and significantly reduced NOx emissions were achieved for hydrogen-rich coal gases under distributed conditions.     

A numerical approach of conjugate hydrogen permeation and polarization in a Pd membrane tube

A numerical method accounting for conjugate hydrogen permeation in a dense palladium (Pd) membrane tube is developed. In the method, hydrogen permeation across the membrane is treated by introducing a source–sink pair and a gas mixture produced from water gas shift reactions serves as the feed gas of the membrane tube. The influences of flow patterns of feed gas and sweep gas as well as their flow rates on hydrogen separation are investigated. A concentration polarization index (CPI) is also conducted to indicate the extent of polarization along the membrane surface. The predicted results suggest that counter-current modes are able to give the better performance of hydrogen separation compared to co-current modes, and hydrogen can be completely recovered if the flow rate of feed gas is low to a certain extent. However, lower flow rates of feed gas and sweep gas will trigger serious concentration polarization. With counter-current modes, the feed gas sent into the membrane tube from the lumen side or the shell side is flexible. The optimum Reynolds number of sweep gas in accordance with the Reynolds number of feed gas is correlated by an arctangent function. This provides a useful reference for the operation of hydrogen separation by controlling sweep gas.     

A new approach in MPPT for photovoltaic array based on Extremum Seeking Control under uniform and non-uniform irradiances

This work presents a Maximum Power Point Tracking (MPPT) based on analyzing the output characteristics of PV array under uniform irradiance and partial shading conditions. In order to carry out MPPT in PV panels, under partial shading conditions a method based on Extremum Seeking Control (ESC) is introduced. In contrast with classic ESC, in this method the double of dithering signal frequency is not used, consequently PV output power has a ripple of a lower frequency. Also the drop which occurs when MPPT system starts to operate in classic ESC method is minimized in this paper. The ESC approach for MPPT in this paper uses a series combination of a Low Pass Filter (LPF) and a High Pass Filter (HPF). These two filters act as a Band Pass Filter (BPF) and let a specific frequency of input power which includes the derivative of PV with respect to its voltage pass through. Finally, the system does not operate in local optimal points for efficient point will be global. The algorithm adds partial shadow judging conditions in ESC method. The system runs the variable step ESC method to realize MPPT when photovoltaic array is under uniform irradiance. Under Partial Shading Condition (PSC), the control method can eliminate the interference of local maximum power point (MPP) to make 23 the PV array running at global MPP. In addition, unlike other methods, the proposed MPPT operates on the global MPPs. The proposed MPP tracker does not add any extra complexity compared to the classical ones. However, it increases significantly the efficiency of the PV installation under PSC. We will show that under uniform irradiance, the proposed MPPT leads to faster performances than classical approaches.     

DFT calculations of double vacancies MoS2 catalyzing water gas shift reaction: S vacancies as electron bridge promote electron transfer to H2O and CO molecules

The poor activity of molybdenum disulfide (MoS₂) basal plane is the key scientific problem to limit efficient hydrogen production. In this paper, MoS₂ with single (V_S-MoS₂) and double sulfur vacancy (V_{S, S}-MoS₂) have been constructed and used for water gas shift reaction (WGSR). Results show that the adsorption energy of V_{S, S}-MoS₂ for CO molecule is −1.43 eV, which is 28.6 times and 1.1 times than original MoS₂ and V_{S, S}-MoS₂, respectively. The energy barrier of the rate-determining step in the association mechanism of V_{S, S}-MoS₂ is 1.78 eV, which is 21% lower than pristine MoS₂. S vacancies are active sites for CO and H₂O adsorption. The delocalized electrons enrichment in S vacancies break the electron transfer barrier between the surface and molecules. Sulfur vacancies as medium make electrons continuously transferred to CO and H₂O molecules.     

A new and facile approach for the preparation of cross-linked sulfonated poly(sulfide sulfone) membranes for fuel cell application

A new and facile approach has been developed for the preparation of cross-linked sulfonated poly(sulfide sulfone) (SPSSF) membranes. The cross-linking reaction was performed by immersing the SPSSF membranes into polyphosphoric acid at 180 °C for 1.5 h and the cross-linking bond was the very stable sulfonyl group. Cross-linking significantly improved the membrane performance, i.e., the cross-linked membranes showed better mechanical properties, lower water uptake and lower methanol permeability than the corresponding uncross-linked ones, while reasonably high proton conductivity was maintained. For example, for the membrane containing 40 mol% sulfonated moiety, by cross-linking the tensile strength increased from 39 MPa (dry) or 21 MPa (wet) to 44 MPa (dry) or 30 MPa (wet) and the elongation at break from 17% (dry) or 18% (wet) to 65% (dry) or 21% (wet), while the water uptake was reduced from 74 to 38 wt% and the methanol permeability from 7.0 × 10⁻⁷ to 1.6 × 10⁻⁷ cm² s⁻¹ (30 °C). The proton conductivity, however, did not decrease too much (from 0.076 to 0.043 S cm⁻¹ in water at 30 °C).     

A focused approach to reliability, availability and maintainability for critical pressure vessels

A “Review Integrity of Critical Equipment” (RICE) programme is presented here. The programme has a systematic structure that facilitates the development of strategic maintenance plans for critical pressure vessels. The programme is based on the reliability, availability and maintainability (RAM)‘ principles and was developed and applied to Sasol's vessels in order to ensure compliance with future statutory requirements. It is believed that the presented programme will be useful for other users than Sasol.

The programme consists of a number of stages; systematic data acquisition, assessment of data and proposed principle inspection plan, acceptance of the plan followed by detailed planning, actual inspection and evaluation of results. The inspection plan is supplemented by proactive measures to solve suspected problems. The paper includes a case study of a critical scrubber vessel in order to illustrate how the programme works.     

A new recursive neural network algorithm to forecast electricity price for PJM day‐ahead market

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day‐ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi‐step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short‐term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short‐term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd.     

A practical approach to fracture analysis at the trailing edge of wind turbine rotor blades

Wind turbine rotor blades are commonly manufactured from composite materials by a moulding process. Typically, the wind turbine blade is produced in two halves, which are eventually adhesively joined along their edges. Investigations of operating wind turbine blades show that debonding of the trailing edge joint is a common failure type, and information on specific reasons is scarce. This paper is concerned with the estimation of the strain energy release rates (SERRs) in trailing edges of wind turbine blades in order to gain insight into the driving failure mechanisms. A method based on the virtual crack closure technique (VCCT) is proposed, which can be used to identify critical areas in the adhesive joint of a trailing edge. The paper gives an overview of methods applicable for fracture cases comprising non‐parallel crack faces in the realm of linear fracture mechanics. Furthermore, the VCCT is discussed in detail and validated against numerical analyses in 2D and 3D. Finally, the SERR of a typical blade section subjected to various loading conditions is investigated and assessed in order to identify potential design drivers for trailing edge details. Analysis of the blade section model suggests that mode III action is governing and accordingly that flapwise shear and torsion are the most important load cases.Copyright © 2013 John Wiley & Sons, Ltd.     

A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring

The aim of non-intrusive appliance load monitoring (NIALM) is to disaggregate the energy consumption of individual electrical appliances from total power consumption utilizing non-intrusive methods. In this paper, a systematic approach to ON-OFF event detection and clustering analysis for NIALM were presented. From the aggregate power consumption data set, the data are passed through median filtering to reduce noise and prepared for the event detection algorithm. The event detection algorithm is to determine the switching of ON and OFF status of electrical appliances. The goodness-of-fit (GOF) methodology is the event detection algorithm implemented. After event detection, the events detected were paired into ON-OFF pairing appliances. The results from the ON-OFF pairing algorithm were further clustered in groups utilizing the K-means clustering analysis. The K-means clustering were implemented as an unsupervised learning methodology for the clustering analysis. The novelty of this paper is the determination of the time duration an electrical appliance is turned ON through combination of event detection, ON-OFF pairing and K-means clustering. The results of the algorithm implementation were discussed and ideas on future work were also proposed.     

A two-phase flow pattern map for annular channels under a DC applied voltage and the application to electrohydrodynamic convective boiling analysis

An experimental study of tube side boiling heat transfer of HFC-134a has been conducted in a single-pass, counter-current flow heat exchanger under an electric field. By applying 0–8 kV to a concentric inner electrode, the mechanics of EHD induced flow and heat transfer augmentation/suppression have been investigated for flow conditions with inlet qualities of 0% to 60%, mass fluxes from 100 kg/m² s to 500 kg/m² s, and heat flux levels between 10 kW/m² and 20 kW/m². A theoretical Steiner type two-phase flow pattern map for flow boiling in the annular channel under applied DC high voltage is also developed. The flow regimes encountered in the convective boiling process have been reconstructed experimentally and compared with the proposed EHD flow regime map. The results show that when the proposed dimensionless criterion M_d Re² is satisfied, EHD interfacial forces have a strong influence on the flow pattern which is considered to be the primary mechanism affecting the increase in pressure drop and the augmentation or even suppression of heat transfer.     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.     

A novel synthesis approach to partially fluorinated sulfonimide based poly (arylene ether sulfone) s for proton exchange membrane

Innovation of novel low cost proton conductive materials with super acidity has been the ever-increasing thirst for PEMFCs. Sulfonimide groups have the strongest gas-phase super-acidity with excellent thermal and chemical stability. Therefore, a series of partially fluorinated sulfonimide functionalized poly(arylene ether sulfone)s (SIPAES-xx) were successfully synthesized by chemical modification of sulfonated polyarylethersulfone (SPAES). The SPAESs were synthesized first by the direct polymerization of 4,4′ -dichlorodiphenylsulfone (DCDPS), 3,3′-disulfonate-4,4′-dichlorodiphenylsulfone (SDCDPS), and bisphenol. Thereafter, all arylsulfonic acid groups were converted into more acidic sulfonimide acid groups using partial fluorinated fluorosulfonyl imide monomer. The effect of the conversion of arylsulfonic acid function into sulfonimide was evaluated through thermal and chemical analysis. ¹H-NMR, FTIR, TGA, FE SEM, and AFM were used to illustrate the structure, thermal and chemical properties of (SIPAES-xx) membranes. The membranes showed IEC values of 0.78–1.41 mequiv./g with 6.7–40.6% water uptake for 20–40% ionic groups. The synthesized SIPAES-40 membranes showed comparable proton conductivity to Nafion^® 117 at same conditions. Nevertheless, the aromatic sulfonimide remained stable up to 370 °C. Furthermore, the presence of fluorine within the sulfonimide polymer provided high dimensional stability and oxidative durability by protecting the polymer chain from oxidizing radical species. Therefore, the synthesized SIPAES-xx membranes have the potential features as a proton exchange membrane (PEM) materials in the fuel cell.