Mathematical modeling of steam-assisted gravity drainage

A mathematical model for gravity drainage in heavy-oil reservoirs and tar sands during steam injection in linear geometry is proposed. The mathematical model is based on experimental observations that the steam zone shape is an inverted triangle with the vertex fixed at the bottom production well. Both temperature and asphaltene content dependence of viscosity of the drained heavy oil and their impact on heavy oil production are considered. The developed model has been validated using experimental data presented in the literature. It is seen that the oil production rate is affected as the asphaltene content of the crude oil changes as a function of temperature. The oil production rate conforms well to previously published data covering a wide range of heavy oils and sands for gravity drainage.     

Graph-based determination of structural controllability and observability for pressure and temperature dynamics during steam-assisted gravity drainage operation

Steam assisted gravity drainage (SAGD), which is used for the in-situ extraction and recovery of oil sands bitumen, is represented by a distributed parameter system (DPS). The problem of sensor placement and the control of steam chamber growth and oil production, respectively, require analysis of the observability and controllability of the system. In this type of system, parametric sensitivity is traditionally used in lieu of observability, and controllability has not been explored rigorously. In this work, we analyze the pressure and temperature fields of a SAGD model based on detailed reservoir simulations and present a data-driven technique to assess the structural controllability and observability of the system, with a view to determine optimal locations of actuators and sensors. An agglomerative hierarchical clustering technique is used to obtain a spanning tree of the clusters which is partitioned based on an objective function to arrive at a set of spatially contiguous clusters that display similar pressure/temperature dynamics. A Granger causality measure is used to create the linkage amongst the clusters to build a digraph model of the data. The driver nodes of the graph identify locations for actuation which provide full control over the graph, and the root strongly connected components indicate sensor locations which ensure structural observability over the entire graph. We demonstrate the method using data generated from SAGD simulations using the CMG-STARS simulator, identify the sensor and actuator locations required for complete structural observability and controllability of the system, and also provide a method of assessment of partial actuation and in-sensor ranges.     

性能随时间衰减的锅炉蒸汽系统循环调度

针对性能随时间衰减的锅炉蒸汽系统的循环调度问题进行了研究.首先建立了描述该问题的混合整数非线性模型;然后提出了确定各锅炉循环运行状态的时间分段策略以简化问题的求解;最后应用列队竞争算法对该混合整数非线性规划问题进行优化计算.采用某锅炉蒸汽系统循环调度的实例对所提出的方法进行了验证,计算结果表明循环调度优化能够获得比人工随机安排更优的调度方案,节能效果十分明显.     

Integrated QoS-aware resource management and scheduling with multi-resource constraints

In dynamic real-time systems such as sensor networks, mobile ad hoc networking and autonomous systems, the mapping between level of service and resource requirements is often not fixed. Instead, the mapping depends on a combination of level of service and outside environmental factors over which the application has no direct control. An example of an application where environmental factors play a significant role is radar tracking. In radar systems, resources must be shared by a set of radar tasks including tracking, searching and target confirmation tasks. Environmental factors such as noise, heating constraints of the radar and the speed, distance and maneuverability of tracked targets dynamically affect the mapping between the level of service and resource requirements. The QoS manager in a radar system must be adaptive, responding to dynamic changes in the environment by efficiently reallocating resource to maintain an acceptable level of service. In this paper, we present an integrated QoS optimization and dwell scheduling scheme for a radar tracking application. QoS optimization is performed using the Q-RAM (Baugh, 1973, ghosh-et al.,2004a approach. Heuristics are used to achieve a two order magnitude of reduction in optimization time over the basic Q-RAM approach allowing QoS optimization and scheduling of a 100 task radar problem to be performed in as little as 700 ms with only a 0.1% QoS penality over Q-RAM alone. Sourav Ghosh received the B.Tech degree in Electronics and Electrical Communications Engineering from Indian Institute of Technology, Kharagpur, India, in 1997, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, USA, in 1999 and 2004 respectively. He is currently working as a Technical Staff at Oracle in Cluster Database Group (RAC). His research interest includes OS resource management and scheduling, performance analysis, Quality of Service (QoS) and real-time systems.     

Single-machine scheduling with advanced process control constraints

With increasing worldwide competition, high technology manufacturing companies have to take great care to lower their production costs and guarantee high quality at the same time. Advanced process control (APC) is widely used in semiconductor manufacturing to adjust machine parameters so as to achieve satisfactory product quality. When there is a conflict between quality and scheduling objectives, quality usually takes precedence. This paper studies the interaction between scheduling and APC. A single-machine multiple-job-types makespan problem with APC constraints is proved to be NP-hard. For some special cases, optimal solutions are obtained analytically. In more general cases, the structure of optimal solutions is explored. An efficient heuristic algorithm based on these structural results is proposed and compared to an integer programming approach.     

动力煤选煤厂分级重选产品结构优化

通过分析多工艺动力煤选煤厂各分选系统,建立了产品结构优化模型;通过平移分配曲线,求得在不同分选密度下的产品结构公式;改变原煤的入洗方案,在不同的原煤入洗率情况下,预测各初级产品的产率和灰分,进而计算吨原煤经济效益;采用差分进化算法求解在取得最大经济效益时的各分选密度及入洗率,得到最优产品结构。应用结果表明,基于差分进化算法的动力煤选煤厂分级重选产品结构优化模型的计算结果符合实际情况。     

一种传输能量固定的数据聚集调度近似算法

针对现有的基于物理干扰模型的数据聚集调度近似算法具有较高的延时问题, 提出了一种改进的传输能量固定的数据聚集调度近似算法。该算法首先根据节点的通信半径构造一个网络通信图, 然后在该通信图中构造一棵数据聚集树, 使节点根据数据聚集树分层进行数据调度。在数据调度过程中, 首先将整个网络划分为若干个圆形区域, 然后对各个区域进行着色, 最后对颜色相同的区域同时进行节点的数据调度。理论分析表明, 该算法的延时上界为（X+1）²(Δ+12R+4)+R。仿真模拟结果表明, 该算法产生的数据聚集延时远低于现有算法。     

Optimal control data scheduling with limited controller-plant communication

This paper considers optimal control data scheduling for finite-horizon linear quadratic regulation (LQR) control of scalar systems with limited controller-plant communication. Both the single-system and multiple-system scenarios are studied. For the first scenario, we derive the necessary and sufficient condition for a comparison function to be positive. Using this condition, the optimality of an explicit schedule is extended from unstable systems in the existing work to general systems. For the second scenario, we are able to construct explicit optimal scheduling policies for three particular classes of problems. Numerical examples are provided to illustrate the proposed results.     

Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.     

Predictive control and scheduling codesign in network control systems

A codesign approach combining predictive control compensation and network scheduling is presented in this paper to overcome the adverse influences of stochastic time delays and packet losses encountered in network-based real-time control systems. The state estimation and control prediction compensation algorithms are used for the random network delays in the feedback and forward channels, and the stability criteria are analyzed. The proper sampling rate is given with network scheduling to meet the desired system performance, while the network-induced delay is tolerated. Simulations show that the codesign approach works well with the bounded network delay.     

Dual scheduling and quantised control for networked control systems with communication constraints

A novel integrated design scheme of average dwell time scheduling strategy, dynamic bandwidth allocation policy and quantised control for a collection of networked control systems (NCSs) with time delay and communication constraints is proposed in this paper. A scheduling policy is presented to accommodate the limitation of communication capacity which depends on the convergence rate of closed-loop system and divergence rate of open-loop plant. Linear programming technique is adopted to dynamically allocate bit rate for each node and the strategy is used to make trade-offs between the network utilisation and the control performance which provides an effective way of optimising the quality of control (QoC) and the quality of service (QoS) for NCSs. Mid-tread uniform quantisers update the quantisation rules according to the assignment of the bit rate and convert the quantised state into a kind of input saturation with bounded disturbances. Taking into account the effect of dual scheduling strategy and quantisation, the NCSs are modelled as discrete-time switched systems with bounded disturbances. Furthermore, a scheduling and quantised feedback control co-design procedure is proposed for the simultaneous stabilisation of the collection of networked subsystems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.     

不确定环境下码头堆场设备集成调度优化

集装箱码头堆场设备调度优化中,对确定条件下的内集卡和场桥的联合调度研究较多,且没有考虑外集卡的随机到达情况。考虑内集卡和场桥作业过程中的不确定性因素,包括：内集卡行驶速度,场桥行走速度和作业时间,并考虑外集卡随机到达堆场对于内集卡调度作业的影响,构建了不确定因素条件下的堆场设备集成调度优化模型,其优化目标是在考虑外集卡随机到达的情况下,最优化堆场设备的作业时间。设计了求解模型的粒子群算法,并比较了一般确定性模型和考虑不确定因素优化模型的结果。算例结果表明,所建立的模型和算法能有效真实地反映不确定因素对集装箱码头堆场设备作业的影响。     

有新鲜度限制的易腐品生产-配送协同调度

为提高按订单生产模式下快速易腐品的生产/配送效率,在兼顾企业运营成本和顾客对产品新鲜度要求的情况下,以总配送成本最小和交付产品总的新鲜度最大为目标,建立了具有最低新鲜度限制的易腐品生产-配送协同调度双目标优化模型,并设计了基于双子串编码的带精英策略的非支配排序遗传算法。首先,描述了客户时间窗和交付产品新鲜度,定义了多产品情形下的客户产品平均新鲜度,建立了生产-配送协同调度双目标模型。然后,对约束条件和两个目标函数进行了转换,采用双子串的染色体编码方式,以及带精英策略的非支配排序遗传算法的算子结构和进化框架对问题进行求解。最后,基于算例分析,对所设计的算法与基于Pareto的模拟退火算法的计算结果进行对比。实验结果表明,两个目标存在效益悖反现象,且所设计的算法能够获得有效的Pareto前沿解集;对产品最低新鲜度的敏感性分析表明,在使用车辆数量较少时,最低新鲜度限制约束对总配送成本和交付产品总的新鲜度有显著影响。     

Production scheduling of parallel machines with model predictive control

This paper considers the problem of optimizing on-line the production scheduling of a multiple-line production plant composed of parallel equivalent machines which can operate at different speeds corresponding to different energy demands. The transportation lines may differ in length and the energy required to move the part to be processed along them is suitably considered in the computation of the overall energy consumption. The optimal control actions are recursively computed with Model Predictive Control aiming to limit the total energy consumption and maximize the overall production. Simulation results are reported to witness the potentialities of the approach in different scenarios.     

资源约束系统的控制与调度协同优化方法

对于一类通信约束的网络控制系统, 应用通信序列的概念和混合逻辑动态的构架, 将其建模成一类集成控制和调度的资源约束系统模型. 采用线性矩阵不等式来描述离散周期系统的周期通信序列和H_∞ 控制的协同优化问题, 并用一种递增优化序列算法来求解优化的调度策略及其相应的渐近稳定及r指数稳定的H_∞控制器. 该算法结合线性矩阵不等式凸优化问题解决了调度与控制的协同设计问题, 同时也节省了搜索优化解的计算时间.     

基于神经网络的注塑机注射速度的迭代学习控制

对具有不确定性和干扰项的重复非线性注塑机控制系统,尤其是注射速度的控制,提出基于神经网络的迭代学习控制器,其中迭代学习控制器设计为神经网络控制器,它以前馈方式作用于对象。PD反馈控制器用于使系统达到稳定,同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。仿真结果表明,该控制器可以随着迭代次数的增加有效减小跟踪误差。     

基于移动轨迹的集装箱码头中控调度研究*

根据重庆五大内陆港口实际作业现状,以组合业务模式下作业效率低、机械损耗高的问题为出发点,提出了一种基于移动轨迹的中控调度方式。该方法将集装箱在码头内部的实际作业路线抽象为有向路径,并通过遗传算法对其进行遗传迭代;在遗传算法求解过程中,提出了一种变长染色体多参数交叉编码的基因编码方式,并对初始化种群进行有效的约束选择。最后通过有向邻接矩阵表达了遗传迭代过程,并通过应用实例中的资源数据对比证明了方法的优越性。     

三冲量控制系统的设计及应用

详细地介绍了蒸汽负荷和给水流量的突然变化对高压汽包液位的影响，对传统的单冲量和双冲量控制在克服假液面和给水干扰方面的缺点进行了深刻的剖析，对具体的装置给出了三冲量控制系统的设计方案及参数计算方法，并简单地介绍了该控制系统的配置。     

带返工的定点装配车间生产计划与调度集成优化

针对定点装配车间不合格品返工导致生产计划及调度不可行的情况,提出一种基于返工延后处理的定点装配车间生产计划与调度集成优化方法.首先制定返工延后处理的粗生产计划;下达计划生产后,将每周期产生的不合格品放入缓冲区,在下周期初时重新调整生产计划并求解新计划下的最优调度,判断其是否满足装配班组负载率要求,不断交替迭代生产计划与调度直至达到计划与调度的均衡和优化.最后通过算例验证了所提出方法的可行性和有效性.     

Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty

This paper addresses the problem of finding a robust and stable schedule for a single machine with availability constraints. The machine suffers unexpected breakdowns and follows the Weibull failure function. A joint model for integrating run-based preventive maintenance (PM) into the production scheduling problem is proposed, in which the sequence of jobs, the PM times and the planned completion times of jobs are proactively determined simultaneously. Aiming at optimizing the bi-objective of system robustness and stability, a genetic algorithm based on the properties of the optimal schedule is proposed. The experimental results demonstrate that the proposed algorithm is efficient and effective under practical problem sizes. In addition, the impact of degree of uncertainty on the performance and the tradeoff between robustness and stability are explored in detail.