深圳抽水蓄能电站引水系统灌浆质量控制与分析

抽水蓄能电站引水系统灌浆质量控制,决定了水道系统渗水量的大小,关系到电站长期运行的经济指标及水道安全。详细介绍了深圳抽水蓄能电站灌浆参数的设计和灌浆中采取的管理措施与技术措施,分析了灌浆检测成果。电站投入运行以来,引水系统结构稳定,无渗漏。通过总结深圳抽水蓄能电站灌浆过程,对水道引水系统灌浆质量控制提出了几点建议,可供类似工程借鉴。     

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

This paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L₂‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.     

导热油在线再生装置的介绍与计算分析

聚酯工业中液相导热油在高温使用环境中会发生裂解甚至劣化的现象,从而影响导热油的使用性能。对劣化后的合成导热油的再生工艺流程进行了说明,提出了理论推导得到的导热油中低沸物质量分数、在线再生量、低沸物初始质量分数和再生批次之间的关系式。然后根据具体案例进行了导热油在线再生的计算分析,得出结论:导热油再生比更换导热油有较大的经济性优势;在导热油再生中确定合理的再生低沸物质量分数,能够以较小的能耗代价完成再生导热油的再生;另外选取适当大一点的单批次再生量,也有利于降低再生过程的能耗。     

Adaptive compensation of persistent actuator failures of nonlinear systems

In this article, adaptive compensation designs are developed for nonlinear systems with uncertainties from the system functions and persistent actuator failures of characterizations that (i) some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants and (ii) the failure pattern always switches from one to another and the switching does not stop. Such a controlled plant is described by an uncertain time-varying nonlinear system, and some robust adaptive feedback linearization based failure compensation results are studied for closed-loop system stabilization and bounded output tracking for some specific conditions. To improve the tracking performance in the presence of persistent actuator failures, a new adaptive control scheme is developed, using the failure indicator function which contains the failure pattern and failure time in the formulation. Detailed stability and tracking performance are shown. Simulation results are shown to verify the effectiveness of the proposed adaptive actuator failure compensation method.     

Adaptive finite-time tracking control for parameterized nonlinear systems with full state constraints

In this article, the issue of adaptive finite-time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property of logarithmic function, a one-to-one nonlinear mapping is constructed to transform a constrained system into an unconstrained system with the same structure. The nonlinear filter is constructed to replace the first-order linear filter in the traditional DSC, and the demand on the filter time constant is reduced. Based on finite-time stable theory and using modified DSC, the finite-time controller is designed via DSC. Theoretical analysis shows that all the signals in the closed-loop system are semiglobal practical finite-time stable. Furthermore, none of the states are outside the defined open set. In the end, simulation results are presented to demonstrate the effectiveness of the proposed control schemes with both linear filters and nonlinear filters.     

A machine learning‐based operational control framework for reducing energy consumption of an amine‐based gas sweetening process

This study proposes a data‐driven operational control framework using machine learning‐based predictive modeling with the aim of decreasing the energy consumption of a natural gas sweetening process. This multi‐stage framework is composed of the following steps: (a) a clustering algorithm based on Density‐Based Spatial Clustering of Applications with Noise methodology is implemented to characterize the sampling space of all possible states of the operation and to determine the operational modes of the gas sweetening unit, (b) the lowest steam consumption of each operational mode is selected as a reference for operational control of the gas sweetening process, and (c) a number of high‐accuracy regression models are developed using the Gradient Boosting Machines algorithm for predicting the controlled parameters and output variables. This framework presents an operational control strategy that provides actionable insights about the energy performance of the current operations of the unit and also suggests the potential of energy saving for gas treating plant operators. The ultimate goal is to leverage this data‐driven strategy in order to identify the achievable energy conservation opportunity in such plants. The dataset for this research study consists of 29 817 records that were sampled over the course of 3 years from a gas train in the South Pars Gas Complex. Furthermore, our offline analysis demonstrates that there is a potential of 8% energy saving, equivalent to 5 760 000 Nm³ of natural gas consumption reduction, which can be achieved by mapping the steam consumption states of the unit to the best energy performances predicted by the proposed framework.     

Control of viscous fingering and mixing in miscible displacements with time-dependent rates

In miscible displacements encountered in enhanced oil recovery processes, the unfavorable viscosity contrast between injected solvent and oil usually leads to viscous fingering (VF), a hydrodynamic instability which may result in a lower sweep efficiency and oil recovery. This phenomenon can be observed in a wide range of flows in subsurface porous media. This study examined a simple cyclic time-dependent displacement rate and its effects on the onset and longer development of VF. It is found that such varying displacement rate can either stabilize or destabilize VF, depending on the cycle period, amplitude, and displacement scenarios. The most important mechanism is that such time-dependent rate can effectively change the competition between convection (destabilizing effect) and dispersion (stabilizing effect). This is different from the widely used constant injection rate where the flow instability is actually determined by the Peclet number and mobility contrast for a given scenario. This study therefore provided a new aspect to control VF, either enhance or reduce, with low additional costs. It is therefore both scientifically and practically important for a wide range of flows in subsurface porous media. © 2017 American Institute of Chemical Engineers AIChE J, 65: 360–371, 2019