生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。     

Scheduling distributed flowshops with flexible assembly and set-up time to minimise makespan

The scheduling problems under distributed production or flexible assembly settings have achieved increasing attention in recent years. This paper considers scheduling the integration of these two environments and proposes an original distributed flowshop scheduling problem with flexible assembly and set-up time. Distributed production stage is deployed several homogeneous flowshop factories that process the jobs to be assembled into final products in the flexible assembly stage. The objective is to find a schedule, including a production subschedule for jobs and an assembly subschedule for products, to minimise the makespan. Such a scheduling problem involves four successive decisions: assigning jobs to production factories, sequencing jobs at every factory, designating an assembly machine for each product and sequencing products on each assembly machine. The computational model is first established, and then a constructive heuristic (TPHS) and two hybrid metaheuristics (HVNS and HPSO) are proposed. Numerical experiments have been carried out and results validate the algorithmic feasibility and effectiveness. TPHS can obtain reasonable solutions in a shorter time, while metaheuristics can report better solutions using more yet acceptable time. HPSO is statistically comparable yet less robust compared with HVNS for small-scale instances. For the large-scale case, HPSO outperforms HVNS on both effectiveness and robustness.     

球磨过程中介质群运动区域划分方法研究

球磨过程中介质群运动状态变化对物料的破碎效率影响极大，通过对介质群运动状态进行区域划分，探究不同球磨工况下的介质群运动区域特征更能有效揭示介质群对物料的破碎方式和有效破碎区域。针对理论划分介质群运动区域过于理想、单一，试验追踪法成本过高的问题，提出了一种介质群运动区域划分的新方法-EDEM网格划分法。首先把筒体的横截面划分为若干个微元，利用数理统计的方法得到介质群位置概率分布函数，获取介质群运动速度区域分布图和碰撞特性区域分布图。然后给出了回转运动、螺旋运动及复合颤振运动三种工况下介质群运动区域的划分实例，并与试验追踪法对比，验证了EDEM网格划分法的准确性和有效性。最后通过粉磨试验中进料的破碎速率和微细颗粒产率探讨了介质群运动区域特性对颗粒的破碎方式和有效破碎区域的影响效果。本研究为优化球磨过程的影响因素和操作参数提供了一种快捷、有效的方法。     

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.     

数字化羊绒测色法及其在长度测量中的应用

针对光电法测试纤维长度等性质时,存在纤维的透光及反光信号与颜色高度相关的问题,采用数字化方法表征羊绒的颜色类别并精准地测量羊绒的长度。将彩色光电耦合器作为测色传感器,搭建了更适合纤维材料的测色装置;通过颜色模型在3种色空间的转换,得到3类颜色的羊绒在三维色空间的分布;基于系列实验,创建了有色羊绒的光学参数即无穷厚时的表观反射率表观反射率与测色装置计算的亮度指标之间的换算模型,并用实验证明采用测得的表观反射率可计算得到准确的须丛线密度曲线,从而可用光电法精准测量有色羊绒的长度。结果表明,该测色方法的测样面积大,代表性好,成本低,同时可用于其他纤维和纺织品的颜色测量。     

A variable neighborhood search heuristic algorithm for the double vehicle routing problem with multiple stacks

This paper addresses the double vehicle routing problem with multiple stacks (DVRPMS) in which a fleet of vehicles must collect items in a pickup region and then travel to a delivery region where all items are delivered. The load compartment of all vehicles is divided into rows (horizontal stacks) of fixed profundity (horizontal heights), and on each row, the unloading process must respect the last‐in‐first‐out policy. The objective of the DVRPMS is to find optimal routes visiting all pickup and delivery points while ensuring the feasibility of the vehicle loading plans. We propose a new integer linear programming formulation, which was useful to find inconsistencies in the results of exact algorithms proposed in the literature, and a variable neighborhood search based algorithm that was able to find solutions with same or higher quality in shorter computational time for most instances when compared to the methods already present in the literature.     

磁阀式可控电抗器的损耗计算与分析

磁阀式可控电抗器(Magnetic-valve controllable reactor, MCR)具有直流偏磁、绕组电流谐波大和磁路中磁通密度不同的特点,这些特点不仅导致其损耗计算方法与电力变压器不同,而且使准确计算MCR的铁芯和绕组损耗比较困难。本文将铁芯分区与J-A动态逆模型相结合,提出MCR铁芯损耗的计算方法;考虑漏磁和谐波的影响,分析推导得到MCR的绕组损耗系数的计算方法和表达式。最后,通过实例的仿真和实验,验证了提出的铁芯损耗计算方法和绕组损耗系数的正确可靠性。     

New approaches to determine the interface height of fire smoke based on a three-layer zone model and its verification in large confined space

Large confined space has high incidence of fires, which seriously threatens the safety of people working there. Understanding the distribution of smoke in such large space is critical to fire development prediction and smoke control. Three improved methods for the stratification interface prediction of fire smoke are developed, including of improved intra-variance, integral ratio and N-percentage methods. In these methods, the interface height is determined by the vertical temperature distribution based on a three-layer smoke zone model, which is an improvement of a two-layer zone model. Thereafter, the three improved methods are applied to several typical fire cases simulated CFD to predict the smoke interface, and their applicability and reliability are verified by comparison of the smoke stratification results with the filed simulation results. Results show that the three improved methods can effectively determine the location of the three-layer zone model's interface, and they have the ability to predict smoke interface for fires with different fire source types and ventilation conditions.     

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.     

Top-k-size keyword search on tree structured data

Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its algorithms as the number of keywords and the size and complexity of the data increase. To face this challenge most of the known approaches restrict their ranking to a subset of the LCAs (e.g., SLCAs, ELCAs), missing relevant results.In this work, we design novel top-k-size stack-based algorithms on tree-structured data. Our algorithms implement ranking semantics for keyword queries which is based on the concept of LCA size. Similar to metric selection in information retrieval, LCA size reflects the proximity of keyword matches in the data tree. This semantics does not rank a predefined subset of LCAs and through a layered presentation of results, it demonstrates improved effectiveness compared to previous relevant approaches. To address performance challenges our algorithms exploit a lattice of the partitions of the keyword set, which empowers a linear time performance. This result is obtained without the support of auxiliary precomputed data structures. An extensive experimental study on various and large datasets confirms the theoretical analysis. The results show that, in contrast to other approaches, our algorithms scale smoothly when the size of the dataset and the number of keywords increase.