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Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on

Date 23-26 May 2011

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Displaying Results 1 - 25 of 121
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • [Back cover]

    Page(s): c4
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    Freely Available from IEEE
  • [Copyright notice]

    Page(s): 1
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    Freely Available from IEEE
  • Greetings from the IPC and NOC chairs

    Page(s): i - viii
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    It is our pleasure to welcome you all to Hangzhou and to the 4th Symposium on Advanced Control of Industrial Processes (Adconip) on behalf of the International Program Committee and National Organizing Committee. The Adconip 2011 is being held from Monday through Thursday, May 23 – 26, 2011 at New Century Resort, Thousand Islands, Hangzhou, China. View full abstract»

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  • Plenary/Keynote sessions

    Page(s): ix - xxiii
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    Summary form only given. Provides an abstract for each of the 21 presentations. View full abstract»

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  • Table of contents

    Page(s): xxiv - lv
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    Freely Available from IEEE
  • Temporal and spatial quantization in nonlinear filtering

    Page(s): 1 - 14
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (861 KB) |  | HTML iconHTML  

    One of the most commonly used tools in systems science is that of nonlinear filtering. Applications can be found in control engineering, telecommunications, radar tracking, environmental systems, economics and many other areas. However, despite the wide spread use of these tools, there remain several unresolved issues. The goal of this paper is to give a brief overview of nonlinear filtering. We give particular emphasis to issues related to temporal and spatial quantization. View full abstract»

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  • Merging process models and plant topology

    Page(s): 15 - 21
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (311 KB) |  | HTML iconHTML  

    The paper discusses the merging of first principles process models with plant topology derived in an automated way from a process drawing. The resulting structural models should make it easier for a range of methods from the literature to be applied to industrial-scale problems in process operation and design. View full abstract»

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  • Diagnosis and control using multiple models. Application to a biological reactor

    Page(s): 22 - 29
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (574 KB) |  | HTML iconHTML  

    Process diagnosis is still considered a challenging engineering problem. Technological systems, and also environmental systems, have complex behaviours often involving non linear relationships. When confronted to such systems, there is a need to build systems which can operate over a wide range of operating conditions. For that, it is very attractive to appeal to a decomposition of the model of the system into a number of simpler linear models. This communication mainly focuses on the use of multiple models for process diagnosis and control. In both cases, it is shown how the traditional tools of the linear automatic can be wide and applied to the structures based on multiple models. View full abstract»

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  • Data reduction and fault diagnosis using principle of distributional equivalence

    Page(s): 30 - 35
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (658 KB) |  | HTML iconHTML  

    Historical data based fault diagnosis methods exploit two key strengths of the multivariate statistical tool being used: i) data compression ability, and ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts[1], and hence is more suited for the task of fault detection and isolation(FDI). In this paper, we propose a methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to CA and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The above aspect is also validated for large scale system using benchmark Tennessee Eastman process simulation case study. View full abstract»

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  • Multiple root cause analysis of linear oscillatory closed-loop single-input single-output (SISO) systems

    Page(s): 36 - 41
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (663 KB) |  | HTML iconHTML  

    In general, oscillatory variables indicate poor performance of control loops. Therefore, diagnosis of the causes for oscillations in control loops is vital for maintaining the product quality within desired limits. In a linear closed-loop SISO system, oscillations can occur due to one or more of the following reasons: (i) poor controller tuning, (ii) control valve stiction and, (iii) external oscillatory disturbances. Several offline data-driven methods have been developed to address the diagnosis problem by focusing on only one of the causes for oscillations. In this work, an algorithm for identification of multiple root causes for oscillations in closed-loop systems is presented. The proposed approach comprises of: (i) Hammerstein based stiction detection algorithm, (ii) amplitude based discrimination algorithm using Hilbert Huang (HH) spectrum for identification of controller and disturbance caused oscillations and, (iii) an algorithm for analyzing the model obtained from Hammerstein approach. A decision algorithm based on the information obtained from the above three components is used for determination of multiple causes for oscillations in linear SISO systems. View full abstract»

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  • Risk based alarm design: A systems approach

    Page(s): 42 - 47
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (193 KB) |  | HTML iconHTML  

    A systems approach to design, analyze and prioritize alarms is proposed. By a system, we refer to a set of variables within a process. An alarm is activated based on the risk associated with the state of the variables in a system. The objectives are to integrate risk estimation with alarm design and to reduce the number of alarms by assigning them to sets of variables instead of single ones. Also based on the relationships among the variables in a system, the future risk associated with the present state of the variables is evaluated. Thus the proposed method has a predictive capability that allows more time to take corrective actions. The applicability of the proposed procedure is demonstrated using the example of a tank process. View full abstract»

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  • Detection of control loop interactions and prioritization of control loop maintenance

    Page(s): 48 - 53
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1274 KB) |  | HTML iconHTML  

    Chemical processes with multiloop control configurations have significant amount of control loop interactions due to tight mass and heat integration. Change in set point and/or controller parameters of one control loop may also affect the variables of other loops. The presence of loop interactions in a process plant can cause significant cost, quality and production losses of the plant. It is challenging to measure the degree of interaction between control loops and rank the loops according to the extent of interactions. This paper provides data driven techniques to determine control loop interactions and rank the loops according to their importance. First, two indices have been developed using integral of absolute or squared error criteria to determine loop interaction and rank of the loops. In another approach, a novel method based on canonical correlation analysis has been developed to calculate interaction among the loops and then normalization is done with respect to the maximum canonical correlation value to determine the rank of the loops. Simulation and experimental results show the validity of the proposed methods. View full abstract»

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  • Multivariate statistical monitoring of multiphase batch processes with uneven operation durations

    Page(s): 54 - 59
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (342 KB) |  | HTML iconHTML  

    In multivariate statistical monitoring, batch process models should well reflect process characteristics in order to achieve satisfactory fault detection results. In manufacturing systems, many batch processes are inherently multiphase. Usually, process features are different from one phase to another, and gradual transitions are often observed between phases. Another important characteristic of batch processes is uneven operation durations. In multiphase batch processes, not only the entire batch durations but also the phase durations may be unequal from batch to batch. In this paper, the Gaussian mixture model (GMM) method is adopted to solve both the multiphase and the uneven-duration problems simultaneously. A benchmark penicillin fermentation process is utilized to verify the phase division, transition identification and process monitoring results based on the proposed method. View full abstract»

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  • An algorithm for fault detection in stochastic non-linear state-space models using particle filters

    Page(s): 60 - 65
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    We propose a novel model-based algorithm for fault detection in nonlinear and non-Gaussian systems. The algorithm utilizes particle filters to generate a sequence of hidden states, which are then used in a log-likelihood ratio test to detect faults. The state-space models considered in this article are not easily amenable to standard log-likelihood ratio test, hence, a novel test statistic based on the joint likelihood function of hidden states and measurements is proposed. The proposed scheme is illustrated through an implementation on a highly non-linear multi-unit chemical reactor system. View full abstract»

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  • Independent component regression based on mutual information maximization

    Page(s): 66 - 71
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (356 KB) |  | HTML iconHTML  

    A new independent component regression (ICR) algorithm which maximizes the mutual information (MI) between extracted latent variables (LV) and output variable is proposed. It is found that mutual information between extracted LVs and output variable can be delicately combined with the independent component analysis (ICA) objective and only two-dimensional joint entropy needs to be estimated, which can be approximated by Edgeworth expansion. Balance is achieved between maximizing statistical independency and mutual information by forming a dual objective optimization problem. The performance of the proposed algorithm is tested on both simulation examples and real data sets. View full abstract»

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  • Output error method for identification of multiple-model process with transition

    Page(s): 72 - 77
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (890 KB) |  | HTML iconHTML  

    This paper is concerned with the identification of multiple model process with transition using the output error (OE) method. Local multiple linear models with output error model structure are identified at fixed operating points first and then a global nonlinear model is approximated by interpolating the multiple linear models with exponential weighting functions. With all the obtained initial values of the system's parameters, nonlinear optimization strategy is implemented to find the optimal values of the parameters for both the local multiple models and their corresponding weighting functions. Finally a more accurate global model can be obtained. The effectiveness of the proposed approach is demonstrated through simulation examples. View full abstract»

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  • Design and control of cyclohexanol reactive-distillation process with alternative decanter configurations

    Page(s): 78 - 83
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (509 KB) |  | HTML iconHTML  

    Cyclohexanol is widely used in industry as a precursor for the synthesis of intermediates of Nylon. In this paper, design and control of reactive-distillation process for the production of cyclohexanol have been studied. The total annual costs of two alternative decanter configurations are comparable. However, one configuration gives much better operability than the others in the face of feed disturbances. To achieve wider operability, proper selection of the overall control strategy is critically important. Rigorous dynamic simulations will be used to illustrate the findings. View full abstract»

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  • Modeling and control system design of an MCFC system

    Page(s): 84 - 89
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (808 KB) |  | HTML iconHTML  

    A molten carbonate fuel cell (MCFC) system is modeled and a control strategy is proposed. A spatially distributed two-dimensional dynamic model of single cell direct internal reforming MCFC was developed as a numerical process by reducing the PDE model to a set of ODE's using the cubic spline collocation method and the finite difference method. Mean cell temperature, Ts;m, maximum temperature difference over a cell, ΔTs;max, oxygen conversion, XO2, and hydrogen mole fraction at the anode outlet, χH2;o, were considered as controlled variables (CV's) under the assumption that their target values are determined by an optimizer in the upper level. Among the CV's, Ts;m and ΔTs;max have much slower dynamics than the other two and keeping ΔTs;max below a certain safety limit is critical. Hence Ts;m and ΔTs;max were designed to be separately controlled under model predictive controller (MPC). On the other hand, XO2 and χH2;o have fast dynamics and were designed to be regulated by single loop PID controllers with feedforward compensation. The manipulated variables (MV's) for each CV group were determined through a system analysis using the singular value decomposition and relative gain array. The performance of the control scheme was evaluated against load (power demand) changes. View full abstract»

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  • Evolutionary algorithm based on the evolution of Pareto archive and individual migration

    Page(s): 90 - 95
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (288 KB) |  | HTML iconHTML  

    An evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed, which is not only suitable for solving multi-objective optimization, but also effective for multimodal function. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multi-objective functions, where individuals generated by tournament selection from the union of single objective and Pareto Archive population form the single objective population of next generation. Especially, individuals in Pareto archive population also join evolutionary operations. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the non-dominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individual migration from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision. Moreover, for multimodal single objective function, simulations also show that ideal optimizing solution can be obtained by properly separating single objective function into multi-objective function and applying the above method. View full abstract»

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  • Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit

    Page(s): 96 - 101
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (479 KB) |  | HTML iconHTML  

    The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts' knowledge. Although some updating algorithms were proposed to solve this problem, it's still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feed-forward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated through the developed DCU operation expert system modeled and optimized on the field data from a real oil refinery. View full abstract»

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  • Improved adaptive steering controller for combined vehicles

    Page(s): 102 - 107
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (518 KB) |  | HTML iconHTML  

    If the dynamics of combined vehicles such as tractor-semitrailer varies greatly, it may be very difficult for inexperienced drivers to achieve good handling stability. Moreover, once combined vehicles become unstable, it is very difficult for all drivers to stabilize vehicles. However, if the behavior of actual combined vehicles can track a designed desired combined vehicle, the good handling property can be maintained even when the dynamics of actual combined vehicles varies large. To achieve handling performance better than the conventional control schemes, the authors have proposed the adaptive steering controller. However, in this conventional scheme, the method to improve tracking performance can not be guaranteed theoretically. Therefore, the trial and error method is required in order to improve robust handling performance. In this paper, to overcome this problem, the authors propose a new adaptive steering controller. View full abstract»

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  • Adaptive fault-tolerant control based on hybrid redundancy

    Page(s): 108 - 113
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (173 KB) |  | HTML iconHTML  

    This paper presents a new adaptive fault-tolerant control system (AFTCS) against actuator failures. The AFTCS utilizes a hybrid of static and dynamic redundancies. In order to maintain the control performance, the redundancy-mode is selected appropriately to remove the effect of the failure. All the switching actions are executed based on only the input and error signals. Hence, any fault detector is not exploited. Furthermore, introducing an adaptive high-gain feedback controller makes it possible to achieve the λ-tracking in the presence of failure. In this paper, several simulation results for the CSTR are shown to confirm the effectiveness of the AFTCS. View full abstract»

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  • Modification of an adaptive controller for systems with input saturation and available output derivatives up to the order of relative degree

    Page(s): 114 - 119
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (351 KB) |  | HTML iconHTML  

    In this paper, the main attention is focused on transient property of control input signal, we propose a novel adaptive controller for time-continuous single-input single-output linear systems with an input saturation in which i-th derivatives of the output signal (i = 1, ..., relative degree) are available. In the proposed adaptive controller, arbitrary deterministic signals can be used as an input of the reference models. It is shown theoretically that the tracking error between the controlled object output and the reference model output can converge to zero when the initial value of the tracking error satisfies a condition. Moreover, it is also shown theoretically that tracking performance can be improved by setting design parameter. View full abstract»

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  • Design of a hard disk drive control system in a multirate system for improvement in the steady-state intersample response

    Page(s): 120 - 123
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (173 KB) |  | HTML iconHTML  

    This paper proposes a new design method for the positioning control system of a hard disk drive (HDD) head. To control the head in an HDD, the control input supplied to the voice coil motor is updated at a fast rate, but the sampling interval of the head position is restricted because of hardware constraints. Hence, the designed control system is a multirate system. In this study, the multirate control system designed for controlling the head in an HDD is extended, and the intersample response is improved independently of the sample response. The effectiveness of the proposed method is demonstrated by a numerical example. View full abstract»

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