NiFe layered double hydroxide as an efficient bifunctional catalyst for electrosynthesis of hydrogen peroxide and oxygen

Two electron oxygen reduction reaction to produce hydrogen peroxide (H₂O₂) is a promising alternative technique to the multistep and high energy consumption anthraquinone process. Herein, Ni–Fe layered double hydroxide (NiFe-LDH) has been firstly demonstrated as an efficient bifunctional catalyst to prepare H₂O₂ by electrochemical oxygen reduction (2e^? ORR) and oxygen evolution reaction (OER). Significantly, the NiFe-LDH catalyst possesses a high faraday efficiency of 88.75% for H₂O₂ preparation in alkaline media. Moreover, the NiFe-LDH catalyst exhibits excellent OER electrocatalytic property with small overpotential of 210 mV at 10 mA cm^?2 and high stability in 1 M KOH solution. On this basis, a new reactor has been designed to electrolyze oxygen and generate hydrogen peroxide. Under the ultra-low cell voltage of 1 V, the H₂O₂ yield reaches to 47.62 mmol g_cat^?1 h^?1. In order to evaluate the application potential of the bifunctional NiFe-LDH catalyst for H₂O₂ preparation, a 1.5 V dry battery has been used as the power supply, and the output of H₂O₂ reaches to 83.90 mmol g_cat^?1 h^?1. The excellent electrocatalytic properties of 2e^? ORR and OER make NiFe-LDH a promising bifunctional electrocatalyst for future commercialization. Moreover, the well-designed 2e^? ORR-OER reactor provides a new strategy for portable production of H₂O₂.     

Automatic design for shop scheduling strategies based on hyper-heuristics: A systematic review

Against the background of smart manufacturing and Industry 4.0, how to achieve real-time scheduling has become a problem to be solved. In this regard, automatic design for shop scheduling based on hyper-heuristics has been widely studied, and a number of reviews and scheduling algorithms have been presented. Few studies, however, have specifically discussed the technical points involved in algorithm development. This study, therefore, constructs a general framework for automatic design for shop scheduling strategies based on hyper-heuristics, and various state-of-the-art technical points in the development process are summarized. First, we summarize the existing types of shop scheduling strategies and classify them using a new classification method. Second, we summarize an automatic design algorithm for shop scheduling. Then, we investigate surrogate-assisted methods that are popular in the current algorithm field. Finally, current problems and challenges are discussed, and potential directions for future research are proposed.     

Structural Aspects of P2-Type Na0.67Mn0.6Ni0.2Li0.2O2 (MNL) Stabilization by Lithium Defects as a Cathode Material for Sodium-Ion Batteries

A known strategy for improving the properties of layered oxide electrodes in sodium-ion batteries is the partial substitution of transition metals by Li. Herein, the role of Li as a defect and its impact on sodium storage in P2-Na_0.67Mn_0.6Ni_0.2Li_0.2O₂ is discussed. In tandem with electrochemical studies, the electronic and atomic structure are studied using solid-state NMR, operando XRD, and density functional theory (DFT). For the as-synthesized material, Li is located in comparable amounts within the sodium and the transition metal oxide (TMO) layers. Desodiation leads to a redistribution of Li ions within the crystal lattice. During charging, Li ions from the Na layer first migrate to the TMO layer before reversing their course at low Na contents. There is little change in the lattice parameters during charging/discharging, indicating stabilization of the P2 structure. This leads to a solid-solution type storage mechanism (sloping voltage profile) and hence excellent cycle life with a capacity of 110 mAh g^-1 after 100 cycles. In contrast, the Li-free compositions Na_0.67Mn_0.6Ni_0.4O₂ and Na_0.67Mn_0.8Ni_0.2O₂ show phase transitions and a stair-case voltage profile. The capacity is found to originate from mainly Ni³⁺/Ni⁴⁺ and O^2-/O^2-δ redox processes by DFT, although a small contribution from Mn⁴⁺/Mn⁵⁺ to the capacity cannot be excluded.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Supply chain planning and scheduling integration using Lagrangian decomposition in a knowledge management environment

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies’ capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach.     

Multi-resource scheduling for FPGA systems

In modern cloud data centers, reconfigurable devices (FPGAs) are used as an alternative to Graphics Processing Units to accelerate data-intensive computations (e.g., machine learning, image and signal processing). Currently, FPGAs are configured to execute fixed workloads, repeatedly over long periods of time. This conflicts with the needs, proper to cloud computing, to flexibly allocate different workloads and to offer the use of physical devices to multiple users. This raises the need for novel, efficient FPGA scheduling algorithms that can decide execution orders close to the optimum in a short time. In this context, we propose a novel scheduling heuristic where groups of tasks that execute together are interposed by hardware reconfigurations. Our contribution is based on gathering tasks around a high-latency task that hides the latency of tasks, within the same group, that run in parallel and have shorter latencies. We evaluated our solution on a benchmark of 37500 random workloads, synthesized from realistic designs (i.e., topology, resource occupancy). For this testbench, on average, our heuristic produces optimum makespan solutions in 47.4% of the cases. It produces acceptable solutions for moderately constrained systems (i.e., the deadline falls within 10% of the optimum makespan) in 90.1% of the cases.     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.