Identification and modelling of aerobic hydrolysis – application of optimal experimental design

Hydrolysis mechanism plays a dominant role in the delicate balance of electron donor/electron acceptor ratios in BNR and EBPR systems as an important carbon source. In this study, the surface‐saturation‐type hydrolysis kinetics was investigated based on respirometric measurements, within the context of the theoretical and the practical identifiability of mathematical models. The identifiable parameters of a selected model were derived from respirograms. In addition, the information from the experiments was evaluated on the basis of Optimal Experimental Design (OED) methodology for different initial conditions of the batch respirometric experiment. © 2003 Society of Chemical Industry     

网络链路性能参数估计的层析成像方法综述

网络层析成像能够在网络内部节点不提供测量协作的情况下,根据端到端的测量结果,间接地估计网络内部链路性能参数,是一种重要的网络测量手段,能直接指导网络管理和网络优化,目前受到国内外学术界和工业界广泛的关注.在广泛收集国内外资料的基础上,首先总结了目前网络层析成像使用的主要端到端测量方法和技术;再根据不同参数对链路性能刻画程度的不同,将链路性能参数的网络层析成像方法分为两类:定量参数推断方法和定性参数推断方法;然后,针对不同类型参数的估计问题,概括分析了现有算法的特点;最后指出该类方法未来的研究方向与潜在的应用前景.     

Closed-loop identification with routine operating data: Effect of time delay and sampling time

In industry, in order to store the reams of data that are collected from all the different flow, level, and temperature sensors, the fast-sampled data is very often downsampled before being stored in a data historian. This downsampled or even compressed data is, then, often used by process engineers to recover the appropriate process parameters. However, little has been written about the effects of the sampling on the quality of the model obtained. Therefore, in this paper, the effects of sampling time are investigated from both a theoretical and practical perspective using results that come out of the theory of closed-loop system identification with routine operating data. It is shown that if the discrete time delay in a process is sufficiently large or the sampling time is sufficiently small, then it is possible to recover the true system parameters. The most common industrial processes that fulfill this constraint are temperature control loops. These results suggest that the sampling time has an important bearing on the quality of the model estimated from routine operating data. Using both Monte Carlo simulations and an experimental set-up with a heated tank, the effect of discrete time delay on the identification of the true continuous time parameters was considered for different sampling times. It was shown that increasing the sampling time above a given threshold resulted in identifying an incorrect model. As well, the models obtained using a PID controller were less sensitive to sampling time than those obtained using a PI controller. However, the PID controller values were more sensitive to the effects of aliasing at larger sampling times.     

多干扰情况下的电力系统闭环辨识研究

电力系统中多处负荷投切与变化等随机性质小扰动,导致系统响应始终存在类似噪声的小幅波动.本文提出利用广域测量类噪声信号辨识被控电力系统模型,及时准确地反映系统当前运行特性,进而解决因仿真模型及参数误差造成的系统分析及控制器设计可信度难以评估等难题.重点对多干扰情况下的系统模型闭环辨识问题进行理论性研究,包括闭环可辨识性、辨识一致性及收敛性等方面,分析得出类噪声环境下要实现系统闭环辨识需具备的条件,并对理论研究结果进行了仿真验证.     

噪声ICA模型去偏技术研究

偏差去除算法通常假设高斯噪声条件下对普通ICA算法进行修正来消除噪声带来的影响。但是存在高斯噪声条件时,普通ICA算法对解混矩阵仍然可以辨识。故引入基于QR分解的RLS自适应噪声抵消算法和Fast-ICA算法相结合,只需对观测信号进行去噪处理,不需要对解混矩阵修正。并分别在同一噪声和相关噪声条件下做了仿真实验,与LMS-ICA算法进行了比较。仿真实验证明,该方法比后者效果显著。提出了用最小二乘算法计算分离信号的输出信噪比,作为评价算法的性能指标。     

Application of evolutionary optimisers in data-based calibration of Activated Sludge Models

Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.     

挖掘回归类的混合模型的可识别性

复杂海量数据往往表现为多种结构特征的混合体，回归类混合模型就是对这种混合体的一个描述．该文基于统计学的有限混合分布理论和可识别性的相关结果，针对回归变量的三种情形：(1)解释变量固定，(2)解释变量随机，(3)解释变量固定且类别参数指定，分别讨论挖掘一般回归类的混合模型的可识别性问题，并给出同族回归类混合模型可识别的相应充分条件．这些条件的一个共同特点是它们都与一类特别的解释变量集合有关，而该类集合是由同族的回归函数与回归参数唯一确定的，其元素使不同的回归参数对应回归函数的相同值．特别地，当回归函数线性时，这类集合就是解释变量空间中的超平面．     

Reprint of “Pharmacokinetic modelling of the anti-malarial drug artesunate and its active metabolite dihydroartemisinin”

A four compartment mechanistic mathematical model is developed for the pharmacokinetics of the commonly used anti-malarial drug artesunate and its principle metabolite dihydroartemisinin following oral administration of artesunate. The model is structurally unidentifiable unless additional constraints are imposed. Combinations of mechanistically derived constraints are considered to assess their effects on structural identifiability and on model fits. Certain combinations of the constraints give rise to locally or globally identifiable model structures.     

基于广域测量系统的电力系统综合区域负荷模型

负荷作为电力系统的一个重要组成部分,其模型的精确程度将在很大程度上影响分析的结果.采用电力系统同步数据对电力系统负荷区域进行建模、网络化简和负荷模型辨识,提出了基于电力系统广域测量系统的综合区域负荷模型的概念,分析了该模型的参数可辨识性.确定了综合区域负荷的网络模型,为区域负荷的建模提供了依据.以一个简单电力系统为例进行仿真计算,结果证明了该负荷建模方法的有效性.