内热型超声雾化溶液再生系统最优内热量的研究

基于质量守恒、能量守恒定律，建立了内热型超声雾化溶液再生系统（IH-UARS）的再生性能预测模型并进行了充分的实验验证，通过研究不同内热量下IH-UARS的再生性能及其变化规律，寻求系统所需的最佳内热量并明确其可能的影响因素。结果表明：IH-UARS系统存在最优的内热量范围，使其再生系统性能最佳；所需最优内热量随着再生溶液流量增大呈显著的对数增长，但受空气流量的影响较弱；在该研究中的标准工况下，IH-UARS所需最优内热量约为275~350 W。此外研究发现：内热量的增长有益于促进初始浓度较高的溶液进一步浓缩再生，如当IH-UARS中内热量增至800 W时，其初始浓度为36%的溶液比24%的溶液浓度增量指标改善幅度高37%。研究所得结果可对提高溶液再生性能及经济性提供积极参考。     

基于WiMAX通信网络的电源监控系统的构建

分析了全球微波接入互存(WiMAX)的技术特点和算法复杂度,探讨了其在电源监控系统中的应用,提出了基于Inter WiMAX Connection 2250技术的远程电源监控系统。在讨论WiMAX物理层算法的基础上,确定了以正交频分多址(OFDMA)模式为主体的物理层通信协议,提高了信道的利用率,抑制了码间干扰,为构建电源监控系统打下了良好的基础。     

State-space ARIMA for supply-chain forecasting

ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.     

Distributed wireless power transfer in sensor networks with multiple Mobile Chargers

We investigate the problem of efficient wireless power transfer in wireless sensor networks. In our approach, special mobile entities (called the Mobile Chargers) traverse the network and wirelessly replenish the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and use limited network information. We propose four new protocols for efficient charging, addressing key issues which we identify, most notably (i) what are good coordination procedures for the Mobile Chargers and (ii) what are good trajectories for the Mobile Chargers. Two of our protocols (DC, DCLK) perform distributed, limited network knowledge coordination and charging, while two others (CC, CCGK) perform centralized, global network knowledge coordination and charging. As detailed simulations demonstrate, one of our distributed protocols outperforms a known state of the art method, while its performance gets quite close to the performance of the powerful centralized global knowledge method.     

泛在电力物联网在北京经济技术开发区的应用探索

泛在电力物联网是物联网在电力领域的具体体现，建设泛在电力物联网是推进"三型两网"建设的关键环节，也是实现能源转型的必要手段。阐述了泛在电力物联网的定义，从供电公司角度出发，根据北京经济技术开发区实际情况，讨论了依托地区特点的泛在电力物联网实施策略和实际应用场景，分析了可能遇到的问题。得出结论：随着泛在电力物联网的深入建设，供电公司能够为电力客户提供多元化服务，提高客户满意度；地市供电公司能够依托泛在电力物联网开展更多电力相关业务；泛在电力物联网的安全性、实用性、有效性是发展面临的首要问题。     

摇摆运动下多环路主冷却剂系统自然循环流量的稳定性分析

海洋核动力平台因其突出的安全性已成为当今核能领域热点研究问题之一，但在海洋等非惯性条件下会使一回路系统的热工特性发生变化。针对此问题，本文对几种典型的一回路系统在摇摆条件下的自然循环流量波动特性进行计算分析。分别建立典型的双环路、三环路、四环路的一回路系统模型布置方案，并同时考虑摇摆中心的位置，根据流体动量守恒方程，得到不同状态下一回路系统内的流量变化规律。对于单堆双环路系统，摇摆中心在船上/中/下部位置时，环路流量波动幅度分别为13.2%/11.2%/9.5%，堆芯流量波动幅度分别为0.9%/0.8%/0.6%；对于单堆三环路系统，摇摆中心在船下部时，环路流量和堆芯流量波动幅度分别为9.2%和0.8%；对于单堆四环路系统，摇摆中心在船下部时，布置方案1和方案2的环路流量波动幅度分别为9.5%和9.2%，堆芯流量波动幅度分别为0.9%和0.7%。计算结果表明：采用单堆双环路的设计布置方案是最有利于系统稳定性的。     

红岭矿18线南西翼通风系统技术改造

针对矿山18线南西翼矿体开采过程中存在的通风系统不完善、风量欠缺、污风无法及时排出等问题，运用风压平衡原理、网络优化技术、机站优化技术以及计算机网络模拟技术展开研究，拟定了3种可行的通风方案。经技术经济比较，确定了新掘18线905～855 m回风井（1.5 m），保留该井905 m 水平回风机站，并在855 m 水平新设1台K45 6 №12风机（18.5 kW）方案。数值模拟结果表明：18线南西翼通风系统总风量达到20.5 m3/s，井下风流的可调性和稳定性增强，为矿山的安全生产提供了有力保障。     

Global optimization for power dispatch problems based on theory of moments

Non-convex of an optimal power dispatch problem makes it difficult to guarantee the global optimum. This paper presents a convex relaxation approach, called the Moment Semidefinite Programming (MSDP) method, to facilitate the search for deterministic global optimal solutions. The method employs a sequence of moments, which can linearize polynomial functions and construct positive semidefinite moment matrices, to form an SDP convex relaxation for power dispatch problems. In particular, the rank of the moment matrix is used as a sufficient condition to ensure the global optimality. The same condition can also be leveraged to estimate the number of global optimal solution(s). This method is effectively applied to {0,1}-economic dispatch (ED) problems and optimal power flow (OPF) problems. Simulation results showed that the MSDP method is capable of solving {0,1}-ED problems with integer values directly, and is able to identify if more than one global optimal solutions exist. In additional, the method can obtain rank-1 moment matrices for OPF’s counterexamples of existing SDP method, this ensures the global solution and overcomes the problem that existing SDP method cannot meet the rank-1 condition sometimes.     

A novel intelligent approach for yaw position forecasting in wind energy systems

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.