Optimization of a continuous distillation process under random inflow rate

The paper deals with a continuous distillation process under stochastic rate of inflows collected in a feed tank. The aim of analysis is to find a robust control of extracting feed from the tank over a certain time horizon such that—without knowledge of future realizations of the inflow rate—some level constraints in the feed tank will be met with high probability. This approach relies on formulating and numerically treating probabilistic constraints. The inflow rate is considered as a stochastic process for which two basically different model assumptions are made: the first model assumes a Gaussian process, and thus reflects the superposition of many independent elementary inflows; the second model treats maybe the simplest case of a single elementary inflow profile, namely rectangular inflows with fixed rate and duration but stochastic starting time. Numerical results illustrating both assumptions are presented, and advantages over the simple anticipation of nominal inflow profiles are highlighted.     

Medium term scheduling of a hydro-thermal system using stochastic model predictive control

A multistage stochastic programming formulation is presented for monthly production planning of a hydro-thermal system. Stochasticity from variations in water reservoir inflows and fluctuations in demand of electric energy are considered explicitly. The problem can be solved efficiently via Nested Benders Decomposition. The solution is implemented in a model predictive control setup and performance of this control technique is demonstrated in simulations. Tuning parameters, such as prediction horizon and shape of the stochastic programming tree are identified and their effects are analyzed.     

Load balancing of dynamical distribution networks with flow constraints and unknown in/outflows

We consider a basic model of a dynamical distribution network, modeled as a directed graph with storage variables corresponding to every vertex and flow inputs corresponding to every edge, subject to unknown but constant inflows and outflows. As a preparatory result it is shown how a distributed proportional–integral controller structure, associating with every edge of the graph a controller state, will regulate the state variables of the vertices, irrespective of the unknown constant inflows and outflows, in the sense that the storage variables converge to the same value (load balancing or consensus). This will be proved by identifying the closed-loop system as a port-Hamiltonian system, and modifying the Hamiltonian function into a Lyapunov function, dependent on the value of the vector of constant inflows and outflows. In the main part of the paper the same problem will be addressed for the case that the input flow variables are constrained to take value in an arbitrary interval. We will derive sufficient and necessary conditions for load balancing, which only depend on the structure of the network in relation with the flow constraints.     

多目标水库资源优化调度决策支持系统及应用

水资源调度是水库运行管理的中心环节和复杂的过程。水库优化调度是涉及入径流、工农业供水、发电等多目标决策问题。运用三库DSS结构设计由数据库、模型库、管理策略方法库和业务操作4部分模块组成的水库资源优化调度决策支持系统,建立基于成本函数和基于决策者偏好的效用函数的目标规划模型,实现水库资源的优化调度策略。     

Neural and adaptive controllers for a non-minimum phase varying time-delay system

In this paper we study a non-minimum phase discrete time system with varying time-delay. We first propose several open loop control architectures based on non-linear neural networks and study their ability to handle the different difficulties of the control problem. All the methods are tested and compared to a baseline linear controller, on a simulated river system. This plant is submitted to perturbations corresponding to water withdrawals and lateral inflows. The above architectures are not able to cope with such perturbations. We then propose a model combining a feed-forward neural network based learning controller and a feedback adaptive controller. The performances of this model are compared to a similar architecture containing linear feed-forward and feedback controllers.     

Data Mining for Generating Predictive Models of Local Hydrology

The problem of downscaling the effects of global scale climate variability into predictions of local hydrology has important implications for water resource management. Our research aims to identify predictive relationships that can be used to integrate solar and ocean-atmospheric conditions into forecasts of regional water flows. In recent work we have developed an induction technique called second-order table compression, in which learning can be viewed as a process that transforms a table consisting of training data into a second-order table (which has sets of atomic values as entries) with fewer rows by merging rows in consistency preserving ways. Here, we apply the second-order table compression technique to generate predictive models of future water inflows of Lake Okeechobee, a primary source of water supply for south Florida. We also describe SORCER, a second-order table compression learning system and compare its performance with three well-established data mining techniques: neural networks, decision tree learning and associational rule mining. SORCER gives more accurate results, on the average, than the other methods with average accuracy between 49% and 56% in the prediction of inflows discretized into four ranges. We discuss the implications of these results and the practical issues in assessing the results from data mining models to guide decision-making.     

A chance-constrained multi-period model for a special multi-reservoir system

We develop a model for optimal multi-period operation of a multi-reservoir system for a basin operating under a conjunctive use of ground and surface water. The inflows to the main reservoir as well as the irrigation demands are stochastic. The ground stock suffers from severe overdrafts increasing the risk of the total depletion of the aquifer in addition to the quality degradation and the threat of seawater intrusion. We treat the uncertainties in the inflows through chance constraints and penalties of failure to release the planned amounts of surface water from the main reservoir. However, we reflect uncertainties in irrigation demands by opting for deficit irrigation and using adequate production functions to estimate the expected crop yields. We attempt in the model to avoid large deficits except perhaps for periods where crop yields are relatively insensitive to water shortage. The objective is to maximize the total expected profit of the entire region. We illustrate the model through an example partially based on some hypothetical data.     

一种获得交叉口分流系数的新方法—极大熵估计法

本文提出了一种根据交叉口的出入口流量来估计交叉口的分流系数的新方法.它是根据信息论的基本原理,在极大熵的意义下,将一个描述不完全问题转化为一个非线性规划问题,从而求出一个最接近真实分流系数的估计值.     

Capacity expansion of linked reservoir systems

A procedure is presented for the analysis of time phasing of reservoir system development. The formulation is based on a multi-purpose reservoir model with stochastic inflows. The object is to select the reservoir sizing, timing of expansion and to establish operating policies such that the total cost associated with the system of linked reservoirs is minimized. The capacity expansion aspect is formulated as a mixed integer continuous linear programming problem. Due to the resulting problem size and its general structure, Benders' decomposition is applied. A numerical example illustrating the procedure is given.     

一类交通网络模型下路段行程时间的解析解

针对一类路段输入流量具有时变特性的路段行程时间进行讨论．所讨论的路段行程时间与路段流量具有指数函数关系．因无法直接求出这类方程关于路段行程时间的解析解，故对指数函数按级数进行展开，得出了路段行程时间以及输出流量随输入流量变化的关系．最后用数值试验对所得结论进行了仿真，结论令人满意．     

An exit-flow model used in dynamic traffic assignment

We consider the behaviour of a link exit-flow model that has been used to model link flows in dynamic traffic assignment (DTA) on networks. In particular, we investigate how the model behaves when time and space (the link length) discretised and the discretisation is varied. We present numerical examples based on various inflow patterns and exit-flow functions and draw conclusions for applications of the model in discrete space and in discrete or continuous time. If inflows are always less than capacity and the link is homogeneous, with no obstructions at the exit, then if the discretisation is refined to the continuous limit the model goes to the solution of the well-known LWR model. However, we observe, somewhat counter intuitively, that the usual continuous-time model does not give as good an approximation to the LWR solution as does the discrete-time model: for a best approximation, the discretisation of space and time should be synchronised. We also investigate the ‘dip’ in outflows, or ‘jamming’ of outflows, that the model displays if inflows are permitted to exceed a capacity limit (as they sometimes do in published applications of the model) and the exit-flow function has a downward sloping part (which has usually been assumed away in DTA applications). In that case, if the number of spatial segments is increased, or the number of time intervals is reduced, then any dip in outflows occurs sooner and is more pronounced, and leads to earlier jamming. In the continuous limit, the jam occurs at the link entrance, preventing inflows in excess of capacity.     

端边云协同的复杂工业过程运行控制智能系统

针对难以建立数学模型的复杂工业运行控制过程,利用可获得的过程控制系统设定值和运行指标以及相关变量的工业大数据和运行控制过程特性,将系统辨识与深度学习相结合,建立以实际运行指标以及相关变量为输入,以实际过程控制系统设定值为输出的运行控制过程数字孪生模型,提出云-边协同的过程控制系统设定值智能控制方法.所提出方法由云-运行控制过程数字孪生模型、边-过程控制系统设定值智能控制模型和自校正机制组成.将工业互联网与工业过程控制系统相结合,提出端边云协同的工业运行控制智能系统的架构和功能,采用所提出控制系统设定值智能控制方法,研制工业过程运行控制智能系统,并在选矿关键设备—–高压辊磨成功应用.所提出系统安全、可靠和优化运行,取得了显著的节能减排效果.     

味精生产过程的计算机组合模型控制系统设计

介绍了一种生物化学产品生产过程的组合模型控制方法。其应用于味精生产过程的控制结果表明 ,这种控制系统在具有一定程度不确定性的非线性系统的控制方面有很高的控制精度。     

高炉冶炼过程计算机控制系统

本文介绍了一个高炉过程控制系统的研制和开发。该系统采用上下位机联合作业方式,系统稳定性好,具有较强的实时数据处理能力,可以方便地对高炉过程的主要参数进行监测和控制,并随时在屏幕流程图中显示过程参数的变化。文中讨论了实现数据采集和处理的有关硬件结构和软件设计,并结合知识工程的概念和方法,探讨了建立高炉诊断和预测系统的若干技术问题。     

Fuzzy scheduling strategy for generalized switched server systems and its robustness over system heterogeneity

Generalized switched server system, a discretely controlled continuous‐time system, in which N tanks are used to represent N parallel entities, respectively, can be employed to address a class of load‐balancing problems. A tank‐pair model is a system that consists of two tanks and a single input single output controller, which regulates the inflows of the two tanks to acquire the two uniform levels under the specified inflow constraints. According to a quantized observation of the N tank levels, some discrete events are generated, and based on certain event feedback strategy, switching the location of the tank‐pair can control all the N tanks in a time‐sharing manner to acquire the N levels uniformity. Different from some existing scheduling strategies, this study proposes a fuzzy scheduling strategy (FSS) for such generalized switched server systems. Special measures are taken to reduce the N‐inputs two‐outputs fuzzy inference to a two‐inputs one‐output one, which greatly facilitates fuzzy scheduler design. Simulation results show that the proposed FSS strategy outperforms over the three existing scheduling strategies as a whole, and they also show that the proposed FSS strategy demonstrates high robustness over system heterogeneity. © 2009 Wiley Periodicals, Inc.     

Economic performance assessment of advanced process control with LQG benchmarking

Economic performance assessment of advanced process control is conducted to investigate performance potentials that can be obtained by control system improvement. An optimization-based approach for economic performance assessment of the constrained process control is integrated with the LQG benchmark in this paper. By explicitly incorporating uncertainty into the performance assessment problem, economic performance evaluation can be formulated as a stochastic optimization problem, which helps to identify the opportunity to improve profitability of the process by taking appropriate risk levels. Using the LQG benchmark to estimate achievable variability reduction through control system improvement, the proposed method provides an estimate of both the performance that can be expected from the improved control system and the operating condition that delivers the improved performance. The results obtained can serve as a tool for control engineers to make decisions on control system tuning and/or upgrading. The proposed algorithm is illustrated via simulation examples as well as an industrial example.     

Generating time-series of dry weather loads to sewers

Availability of appropriate methods for quantifying temporal and spatial variations of inflows to sewer systems is a prerequisite to effective sewer system modelling. To contribute to this goal, an empirical generator of sub-catchment wastewater outputs, for use as flow and water quality inputs to dynamic simulations of the larger sewerage system, is developed and evaluated. The deterministic part of the model is represented by means of Fourier series to generate diurnal profiles and a linear regression to generalise between sites, while a novel application of a multivariate error model with a lag-one autoregression term provides a stochastic component. Using a case study of Bogotá (Colombia), the validities of model assumptions are analysed and model results are compared with available dry weather measurements. The transferability of the methodology to other drainage systems is partially assessed using Linz (Austria) as a case study. It is concluded that the stochastic generator is a useful tool for generating flow and water quality at gauged and ungauged sub-catchment outlets in Bogotá and potentially other catchments.     

Optimality criteria for controlled discontinous processes

The purpose of this paper is to extend the Hamilton-Jacobi criteria for optimality to stochastic control systems with discontinuous system state process. The controlled system process is modeled as a well-measurable process. The admissible class of control processes is not required to be smooth and consists of predictable processes with respect to the available information σ-field. The total cost corresponding to the admissible control law is a measure generated by an integrable increasing process (random clock of the system), which may also depend on the control law. The optimality criterion is established in terms of inequalities involving dual predictable projection of the performance rate and increasing predictable process which generate a potential of class D. For a sufficiently general type of system state process such predictable processes have been calculated, and an explicit version of the optimality criterion is presented.     

Optimal multi-reservoir network control by augmented Lagrange programming neural network

An approach based on augmented Lagrange programming neural network (ALPNN) is proposed for the optimal operation of multi-reservoir network control problems. The main objective here is to find out the optimal hourly water releases from each hydro-plant in the interconnected hydro system to minimize the energy deficit and to distribute uniformly the energy deficit if any in each time interval. The interdependence between water discharge rate variables is very apparent in multi-reservoir network control problems. This proposed method takes into account the concurrent interaction among all the water discharge rate variables of the problem. This approach is based on the Lagrange multiplier theory and search for solutions satisfying the necessary conditions of optimality in the state space. The network equilibrium point satisfies the Kuhn-Tucker condition for the problem and corresponds to the Lagrange solution of the problem. This technique has been applied to a standard 10-reservoir interconnected network in which each hydro-power plant has a linear generation model and discretized time varying river inflows. Results obtained from this approach are compared with those obtained by the conventional discrete maximum principle method. It is observed from the results that the proposed method is very effective and provides better results with respect to constraint satisfaction.     

精细化工过程控制技术的发展动向

精细化工产品的市场需求快速多变,产品更新换代快,使得精细化工生产过程一般采取小批量、多品种生产,生产过程多为间歇或半连续过程,时变性、非线性强,生产过程的工艺流程复杂,操作步骤繁杂,劳动强度大;精细化工生产过程的工艺介质多为有一定毒性、腐蚀性和易燃易爆性的化学品,过程运行危险性大;这些特点使得精细化工生产过程更难以控制,综合自动化水平普遍较低.为了提高精细化工生产过程的综合自动化水平,需根据精细化工生产过程的特点及其控制需求,进行精细化工生产过程控制技术的研究和应用.目前,精细化工过程控制技术的主要发展动向为:从常规仪表控制向计算机控制、集散式控制系统(Distributed Control System,DCS)控制发展;从简单的顺序控制、程序控制向基于ISA SP88间歇过程控制标准的自动批量生产控制发展;从人工控制、常规PID控制向先进的批次-批次迭代学习优化控制发展;从简单工艺参数的控制向生产工况和生产质量的综合性统计过程控制发展;从简单的工艺参数越限连锁报警向综合性安全保护控制和在线实时非正常工况管理发展.