This paper studies the initial boundary value problem of the Hirota–Satsuma system posed on the half line. ...ut−αuxxx+6uux=2βvvx,x>0,t>0,vt+vxxx+3uvx=0,x>0,t>0,ux,0=u0x,vx,0=v0x,x>0,u0,t=ft,v0,t=gt,t>0.. For −1/8<s<3/2,s≠1/2, we demonstrate that the abovementioned system is locally well-posed in Hsℝ×H1+sℝ by utilizing several analytic boundary forcing operators.
In this paper, a modified EMD-FNN model (empirical mode decomposition (EMD) based feed-forward neural network (FNN) ensemble learning paradigm) is proposed for wind speed forecasting. The nonlinear ...and non-stationary original wind speed series is first decomposed into a finite and often small number of intrinsic mode functions (IMFs) and one residual series using EMD technique for a deep insight into the data structure. Then these sub-series except the high frequency are forecasted respectively by FNN whose input variables are selected by using partial autocorrelation function (PACF). Finally, the prediction results of the modeled IMFs and residual series are summed to formulate an ensemble forecast for the original wind speed series. Further more, the developed model shows the best accuracy comparing with basic FNN and unmodified EMD-based FNN through multi-step forecasting the mean monthly and daily wind speed in Zhangye of China.
► Multi-step forecasting for nonlinear and non-stationary wind speed series. ► EMD technique is used for a deep insight into the data structure. ► Use the robust FNN model for forecasting the sub-series obtained from EMD. ► PACF is used to select the input variables for FNNs. ► Get rid of the high frequency to improve forecasting precision.
•First literature review paper of blockchain technology in agri-food value chain management.•Four applications of blockchain technology are identified.•Six challenges for applying blockchain ...technology are identified.•Research gaps and future research directions are proposed.
Agri-food value chain is an area of significant importance because of providing sustainable, affordable, safety and sufficient food, feed, fibre and fuel to consumers, it is critical to ensure these value chains running smoothly and successfully by applying advanced internet technologies. Blockchain technology is a new digital technological approach underpinned by the Industry 4.0 to ensuring data integrity and preventing tampering and single point failure through offering fault-tolerance, immutability, trust, transparency and full traceability of the stored transaction records to all agri-food value chain partners. This paper used systematic literature network analysis to review the state-of-the-art blockchain technology including its recent advances, main applications in agri-food value chain and challenges from a holistic perspective. The findings suggest that blockchain technology together with advanced information and communication technology and internet of things have been adopted for the improvement of agri-food value chain management in four main aspects: traceability, information security, manufacturing and sustainable water management. Six challenges have been identified including storage capacity and scalability, privacy leakage, high cost and regulation problem, throughput and latency issue, and lack of skills. Based on the critical analysis of literature, research gaps and future research directions are proposed in this paper regarding the applications and challenges of blockchain technology in agri-food value chain management. This study makes contributions to the extant literature in the field of agri-food value chain management by discovering the potential of blockchain technology and its implications for agri-food value chain performance improvements such as food safety, food quality and food traceability.
•An advanced forecasting system is proposed for short-term wind speed prediction.•The data pre-processing technique can extract effective information from the raw sequences and eliminate the ...volatility and uncertainty of the data.•Choose the appropriate benchmark models in different situations based on optimal benchmark model selection strategy.•A multi-objective optimizer is applied to find the optimal weights and a theoretical proof indicates that the weights assigned by this optimizer are Pareto optimal solutions.•Our proposed forecasting system can quantify the uncertainty of the wind speed sequences.
Facing the increasing depletion of traditional energy resources and the worsening environmental issues, wind energy sources have been widely considered. As an essential renewable energy resource, wind energy features abundant deposits, extensive distribution, non-pollution, etc. In recent years, wind power generation occupies a non-negligible position in the electric power industry. Stable and reliable power system operation demands accurate wind speed prediction (WSP), but the inherent randomness of wind speed sequences complicates their fluctuations and causes them to be uncontrollable. In this paper, an innovative WSP system is proposed, which combines data pre-processing technique, benchmark model selection, an advanced optimizer for point forecast and interval forecast. Furthermore, this paper theoretically demonstrates that the weights allocated by this optimizer are Pareto optimal solutions. Six interval data from two sites in China are utilized to validate the forecasting performance of our developed model. The experimental results indicate that the developed model can achieve superior accuracy compared to the tested models in all cases for point forecast, and also obtains the forecasting interval with high coverage and low width error, which is an extremely crucial instruction to guarantee the security and stability of the power system.
Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. ...Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.
•An effective analysis of the characteristics of the original data.•The model input structure is determined by phase space reconstruction.•A novel combine model based on linear and nonlinear framework.•Comparative experiments are performed to prove the validity of the model.•The model's applicability and effectiveness are verified in the real wind farm.
Lignin‐derived hierarchical porous carbon (LHPC) was prepared through a facile template‐free method. Solidification of the lignin–KOH solution resulted in KOH crystalizing within lignin. The ...crystalized KOH particles in solid lignin acted both as template and activating agent in the heat‐treatment process. The obtained LHPC, exhibiting a 3D network, consisted of macroporous cores, mesoporous channels, and micropores. The LHPC comprised 12.27 at % oxygen‐containing groups, which resulted in pseudocapacitance. The LHPC displayed a capacitance of 165.0 F g−1 in 1 M H2SO4 at 0.05 A g−1, and the capacitance was still 123.5 F g−1 even at 10 A g−1. The LHPC also displayed excellent cycling stability with capacitance retention of 97.3 % after 5000 galvanostatic charge–discharge cycles. On account of the facile preparation of LHPC, this paper offers a facile alternative method for the preparation of hierarchical porous carbon for electrochemical energy storage devices.
Solid as a rock: 3D hierarchical porous carbon (HPC) with superior rate performance is prepared from alkaline lignin through a facile, template‐free method. In this method, KOH acts both as template and activating agent. The obtained HPC is composed of 3D macroporous cores, mesochannels, and micropores, which endows the obtained HPC with high rate capability and a long lifespan when used in supercapacitors.
Rice husk (RH) is a kind of biomass with huge amount in the world. Comprehensive utilization of RH is of great significance for a sustainable society. In this paper, a green technology to produce ...high capacitance rice husk-based activated carbon (RHC) was developed. High capacitance RHC was prepared by KOH activation of RH. The waste liquid produced in the washing procedure of RHC was transformed into nano-SiO2 by acidification with HCl. The KCl saline solution produced from the preparation of nano-SiO2 was used to obtain water via evaporation and prepare KOH via electrolysis. Importantly, the collected water and KOH was reused in this technology. The obtained RHC possessed a specific surface area as high as 3263 m2 g−1, and exhibited a high specific capacitance of 330 F g−1 in 6 mol L−1 KOH aqueous solution. The nano-SiO2 was amorphous with average diameter of ∼30–40 nm. In addition, the economic analysis demonstrated a significant gross profit margin of 70.72% for the products. The technology is simple, feasible, low-cost and environmentally friendly for large-scale production.
•A green preparation technology of rice husk based activated carbon was developed.•RHC exhibits the highest specific capacitance of 330 F g−1.•The recycle of water and activating agent KOH was realized.•A significant gross profit margin of 71.2% is estimated for the product.•This technology reduces reagent, water consumption and eliminate pollution.
•Use multiple seasonal patterns to pre-process data.•Improve the accuracy and stability simultaneously of electrical load forecasting.•Propose a modified generalized regression neural network (GRNN) ...based on a multi-objective optimization.•Propose a hybrid model based on multiple seasonal patterns and a modified GRNN.
Short-term load forecasting (STLF) plays an important role in the efficient management of electric systems. Building an optimization model that will enhance forecasting accuracy is not only a challenging task but also a concern for electrical load prediction. Especially due to artificial neural networks (ANNs), the final results are dependent on the initial random weights and thresholds, which influence the forecasting stability. Most analyses are based on accuracy improvements, but the effectiveness of a forecasting model is determined equally by its stability. Considering only one criterion (accuracy or stability) is insufficient. Thus, for the model to achieve these two relatively independent objectives at the same time, high accuracy and strong stability, a modified generalized regression neural network (GRNN) based on a multi-objective firefly algorithm (MOFA), employed to optimize the initial weights and thresholds of the GRNN, is proposed. A new hybrid model composed of multiple seasonal patterns, a data pre-processing technique to reduce interferences from the original data, and MOFA-GRNN for electrical load forecasting is successfully developed in this paper. Case studies utilizing half-hourly electrical load data from three states in Australia are used as illustrative examples to evaluate the effectiveness and efficiency of the developed hybrid model. Experimental results clearly showed that both the accuracy and stability of the developed hybrid model is superior to the models compared.
E-bicycles are powered by batteries including lithium-ion, lead–acid, and others. The reuse of waste batteries shows promise for grid-scale storage. The New National Standard for e-bicycles is to be ...introduced in China, that might result in the country becoming the largest source of battery waste in the world. If the waste batteries are not recycled appropriately, it will cause significant heavy metal pollution, which will in turn, pose a serious threat to the ecological environment and human health. This paper discusses the current status of recycling of e-bicycle batteries in China and reviews the current recycling approaches. We developed a waste e-bicycle battery recycling system based on “Internet+” to solve the dilemma of recycling end-of-life batteries; this system has three subsystems: offline reverse logistics recovery system, online network recycling system, and traceability management system. In particular, the participation of consumers and government, reward-penalty mechanism, “Internet +” development, and other strategies are considered to improve recycling systems throughout life cycle of the products. The proposed recycling system can increase the waste battery recycling rate by 2.59% under the reward-penalty mechanism, and reduce carbon dioxide emissions by 58%, which is conducive to promoting sustainable development.
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