A novel quasi-oppositional harmony search algorithm and fuzzy logic controller for frequency stabilization of an isolated hybrid power system

The intermittent wind power in isolated hybrid distributed generation (IHDG) may cause serious problems associated with frequency (f) and power (P) fluctuation. Energy storage devices such as battery, super capacitor, and superconducting magnetic energy storage (SMES) may be used to reduce these fluctuations associated with f and P. This paper presents a study of IHDG power system for improving both f and P deviation profiles with the help of SMES. The studied IHDG power system is consisted of wind turbine generator and diesel engine generator. Both f and P control problems of the studied power system model are addressed in presence or absence of SMES. Fuzzy logic based proportional–integral–derivative (PID) controller with SMES is used for the purpose of minimization of f and P deviations. The different tunable parameters of the PID controller and those of the SMES are tuned by a novel quasi-oppositional harmony search algorithm. Performance study of the IHDG power system model is carried out under different perturbation conditions. The results demonstrate minimum f and P deviations may be achieved by using the proposed fuzzy logic based PID controller along with SMES.     

Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems

In this paper we address the optimal sizing and scheduling of isolated hybrid systems using an optimization framework. The hybrid system features wind and photovoltaic conversion systems, batteries and diesel backup generators to supply electricity demand. A Mixed-Integer Linear Programming formulation is used to model system behavior over a time horizon of one year, considering hourly changes in both the availability of renewable resources and energy demand. The optimal solution is achieved with respect to the minimization of the levelized cost of energy (LCOE) over a lifetime of 20 years. Results for a case study show that the most economical solution features all four postulated subsystems.     

Interaction of tRNA with Eukaryotic Ribosome

This paper is a review of currently available data concerning interactions of tRNAs with the eukaryotic ribosome at various stages of translation. These data include the results obtained by means of cryo-electron microscopy and X-ray crystallography applied to various model ribosomal complexes, site-directed cross-linking with the use of tRNA derivatives bearing chemically or photochemically reactive groups in the CCA-terminal fragment and chemical probing of 28S rRNA in the region of the peptidyl transferase center. Similarities and differences in the interactions of tRNAs with prokaryotic and eukaryotic ribosomes are discussed with concomitant consideration of the extent of resemblance between molecular mechanisms of translation in eukaryotes and bacteria.     

Interactions between hybrid nanosized particles and convection melting inside an enclosure with partially active walls: 2D lattice Boltzmann-based numerical investigation

The ice melting is investigated inside a square cavity with two isothermally partially active walls. The concept of dispersing hybrid alumina–Cu nanoparticles and hybrid silica–multiwalled carbon nanotubes (MWCNTs) nanoparticles is recommended for thermal performance enhancement in this thermal energy storage (TES) system. The two-dimensional explicit lattice Boltzmann convection melting scheme in the single-phase model is applied to account for the natural convection flow induced in the melt region and evolution of the solid–liquid interface. The complete melting time for the pure phase change material (PCM) using case (II) is 33.3% lower compared with other cases. If the price of hybrid Al₂O₃–Cu nanoparticles and heat storage capacity is important, the full melt time diminishes by 16.6% with a volume fraction of 0.01 in case (II). Once hybrid silica–MWCNT nanoparticles with a volume fraction of 0.01 are utilized inside case (II), the lowest charging time is achieved. The complete melting time abates by 23.66% in contrast to the pure PCM melting. The use of single/hybrid nanoparticles to enhance the PCM melting is not necessarily economical as efficient positions of active parts could further lessen the charging time. The efficiency of hybrid nanoparticles is linked to the type and weight proportions of nanoparticles, and positions of thermally active parts.     

Numerical investigation on optimization of thermal analysis due to immersion of hybrid nanostructures in a fluid of shear dependent viscosity using the finite element method

This article considers the dispersion of hybrid and mono nanoparticles in a fluid with viscosity (Williamson) dependent on shear rate, over a heated surface moving with nonuniform velocity and exposed to a magnetic field in the presence of an applied current. Extensive modeling leads to complex coupled mathematical models that are solved numerically via the finite element method (FEM). Convergent simulations are run to investigate the role of parameters on the dynamics of flow fields. The magnetic field intensity plays a role in controlling the magnetohydrodynamic boundary layer thickness (BLT) and thermal radiation controls the thickness of thermal boundary layers (TBL). However, the magnetic field intensity is responsible for an increase in BLT. In contrast to this, thermal radiation plays a role in controlling the thickness of the TBL. The impact of shear rate dependent viscosity on velocity is remarkable for both fluids. The motion of both of the fluids slows down when viscosity varies as a function of shear rate. Viscosity depending on the shear rate has a significant impact on wall shear stress. It is observed from simulations that wall shear increases when the parameters appearing in the model for shear rate dependent viscosity are increased. However, this increase in wall shear stress associated with a hybrid nanofluid is greater than the increase in wall shear stress associated with a mono nanofluid.     

Influence of bunch maturation and chemical precursors on acrylamide formation in starchy banana chips

The present study investigated the effect of ripening stages and chemical precursors on acrylamide formation in deep-fried chips of five plantains and one cooking banana. The highest level of acrylamide was found in the cooking banana, followed by False Horn plantain and French plantain, respectively. French plantain hybrids exhibited a significantly lower (P < 0.05) level of acrylamide when compared to French plantain. The ripening stage demonstrated a positive Pearson correlation (P < 0.05, r = 0.57) with acrylamide formation. As ripening progressed, the levels of glucose and fructose significantly increased (P < 0.05) and showed a positive correlation with acrylamide formation (r = 0.85 and 0.96, respectively). The level of the amino acid asparagine during ripening was not correlated with acrylamide formation. In contrast, the level of histidine, arginine, iso-leucine and cystine during ripening was positively correlated (P < 0.05, r > 0.60) with acrylamide formation in fried chips. The higher level of TP was significantly related (P < 0.05) to the lower level of acrylamide (r = −0.62). The reduced levels of carotenoid isomers, except lutein, during fruit ripening were positively correlated (P < 0.05) with acrylamide formation, especially trans-BC (r = 0.72) and 9-cis-BC(r = 0.64).     

An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.     

聚类混合型数据的密度峰值改进算法

聚类混合型数据，通常是依据样本属性类别的不同分别进行评价。但这种将样本属性划分到不同子空间中分别度量的方式，割裂了样本属性原有的统一性；导致对样本个体的相似性评价产生了非一致的度量偏差。针对这一问题，提出以二进制编码样本属性，再由海明差异对属性编码施行统一度量的新的聚类算法。新算法通过在统一的框架内对混合型数据实施相似性度量，避免了对样本属性的切割，在此基础上又根据不同属性的性质赋予其不同的权重，并以此评价样本个体之间的相似程度。实验结果表明，新算法能够有效地聚类混合型数据；与已有的其他聚类算法相比较，表现出更好的聚类准确率及稳定性。     

基于混合蚁群算法的异质车队低碳VRP研究

针对货运车辆在配送调度过程中产生大量碳排放的问题，建立模型将多种影响碳排放量的因素协同优化。模型中考虑了不同载重量的异质车队，两个节点之间有多条道路的柔性路径，以及车辆重量随卸货而减少的动态负载等因素，以碳排放量、行驶时间和行驶路程为优化目标，并加入了节点需求时间窗、根据速度变化划分路段、交接和卸货时间的约束。提出了一种混合蚁群算法，利用蚁群算法信息素强度更新方式保持群体记忆性，利用粒子群算法的快速收敛特性增加计算效率。通过随机数值算例的仿真优化与对比分析，验证了算法和模型的有效性。