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Process control system design relies in the main part on the suitable modeling and simulation of the underpinning operation of the physical system. The paper presupposes that the qualitative dependency among process variables is the fundamental model that must be generated from quantitative across time observations. Hence, a novel approach to qualitative interpretation of sensory data taken over time from the industrial plant is presented. Contrary to the most current techniques that derive a single qualitative differential equation, the described approach extracts diverse qualitative expressions (correlative knowledge) from the set of observed process variables from many distinctive and interesting time intervals. The technique is based on mapping quantitative time series data to binary coded qualitative difference vectors of process variables that significantly accelerates the modeling process. An experimental evaluation of the technique is shown.

The paper presents an efficient method for deriving qualitative correlative models of physical processes from observations of their behaviors. Most current techniques in qualitative modeling address learning a single qualitative differential equation (QDE) to model the system. Since many complex physical systems do not fall in this oversimplified class, we describe a novel technique that reads quantitative time-series data of process variables and promptly derives a qualitative correlative model. The technique is based on mapping quantitative time series data to binary coded qualitative difference vectors of process variables. The presented procedure significantly accelerates the modeling process. The results of applying the technique to a real-world thermodynamic system are shown.

The biological treatment is the most complex step in removing organic and inorganic pollutants from wastewaters, being very sensitive to input-flow oscillations, operating conditions and biomass evolution. Sudden increases in substrate concentration or some inhibitory substances, deterioration of the biomass, or few observed species, all of these lead to a repeated process identification for each waste and biomass type, and also to a difficult control because of the process complexity, low data reproducibility and flexibility of the aerator-settler unit. The numerical analysis, even under imperfect data, can lead to significant improvements in the wastewater treatment (WWT) by determining the optimum and safe plant operating conditions. When only one bioreactor-settler unit exists, a few number of optimisation criteria can be checked. Alternatively, the paper illustrates in a complex WWT plant case including several units, how the increased system flexibility allows using multiple plant operation policies under various optimisation criteria. The analysis is exemplified in a practical case of three serial WWT units by deriving various transient operating policies with considering compromises among process yield, economics and safety aspects.

The role of mathematical modelling is to give insight into the process being investigated by providing a concise summary of the observed behaviour. Generally speaking, creating a model using input-output data is characterised by two things: one is a mathematical tool to express a system model and the other is the method of identification. Process identification consists of two parts: structure identification and parameter identification. Stmcture identification means finding the input variables that affect the output variables, while parameter identification means finding the values of the parameters of the relationship function.

There are several approaches to nonlinear system identification. The combination of linear dynamic models with static or memory-less nonlinear functions is called block-oriented nonlinear models. The chapter describes the identification of block-oriented nonlinear models and three types of models have been studied in detail: the Hammerstein model, the Wiener model, and the NLN Hammerstein–Wiener model. There are several advantages when using block-oriented models: (1) low cost in identification tests, (2) low cost in identification and control computations, and (3) ease in comprehending and incorporating a priori process knowledge. The shortcoming of block-oriented models is the lack of a global description of the system. The Hammerstein model is a block-oriented nonlinear model with a static nonlinear function followed by a linear dynamic block. The Wiener model is a nonlinear model with a linear dynamic block followed by a static nonlinear function. The NLN Hammerstein–Wiener model is the combination of the Hammerstein and the Wiener model consisting of a linear block embedded between two static nonlinear gains. The NLN model can be easily extended to the multi-input–single-output (MISO) case if there is no joint nonlinearity among the inputs.

Opisana je identifikacija nelinearnih vremenski promjenljivih procesa, pri čemu je posebna pažnja posvećena primjeni neuronskih mreža u identifikaciji nelinearnih procesa. Objašnjena je veza između općeg ulazno-izlaznog nelinearnog modela i statičkih neuronskih mreža. Detaljno su opisane korištene strukture neuronskih mreža: višeslojna perceptronska mreža (MLP), neizrazita neuronska mreža (FNN) i CMAC mreža. Opisani su i ispitani nerekurzivni i rekurzivni algoritmi učenja neuronskih mreža. Prezentiran je algoritam trenutačne linearizacije. Predložen je sustav prediktivnog upravljanja koji se sastoji od nelinearnog modela zadanog procesa realiziranog neuronskom mrežom, postupka trenutačne linearizacije i poopćenog prediktivnog regulatora. Svojstva sustava upravljanja ispitana su simulacijom na računalu na matematičkom modelu nelinearnog sustava. Temeljem jednadžbi očuvanja tvari i energije izveden je fizikalni model šaržne destilacijske kolone. Model je uspoređen s eksperimentalnim rezultatima dobivenim na laboratorijskoj destilacijksoj koloni. Simulacijom na računalu provjerena je primjena predloženog sustava prediktivnog upravljanja pri upravljanju šaržnom destilacijksom kolonom. System identification of non-linear time variant processes is presented, with special attention given to the identification of non-linear processes using neural networks. Connection between black box input-output non-linear models and static neural networks is described. Structures of MultiLayer Perceptron (MLP), Fuzzy-Neural Network (FNN) and Cerebellar Model Articulation Controller (CMAC) are discussed in great detail. On-line and off-line learning algorithms for these neural networks are described and tested. Instantaneous linearization method is derived. System of predictive control is given, which is comprised from nonlinear neural model of given process, instantaneous linearization method and generalized predictive controller. Characteristics of given system were proved through computer simulations. Mathematical model of batch distillation column based on heat and material balances is derived. Model is compared with the experimental results obtained on a laboratory distillation column. Predictive control of batch distillation column was tested through computer simulations.

This paper presents a neural-network approach to operator-independent assessing the operational states of chemical semibatch reactors. The suitability of neural networks for process monitoring was investigated in a miniplant in which strongly exothermic chemical reference processes were carried out. Before being applied to state classification, the neural-network classifiers first have be trained using process data of normal and abnormal sequences of reaction to establish a non-linear decision model between process parameters and state classification. Afterwards, the trained classifiers can be used for process monitoring. Best results were reached with three-layer perceptron networks. For assessing the danger potential of fault states, separate perceptron networks for danger classification and for fault identification were used.

In open-loop optimal control, inputs to a dynamic system are computed that optimize a specified performance criterion. A novel approach is proposed that quantifies the impact of parameter and control implementation inaccuracies on the performance of open-loop control policies. Such information can be used to decide whether more laboratory experiments are needed to produce parameter estimates of higher accuracy, or to define performance objectives for lower-level control loops that implement the optimal control trajectory. The novel features of the approach include (1) computational efficiency; (2) the parametric uncertainty description, which is produced by most process identification algorithms; and (3) addressing of control implementation inaccuracies. The merits of the approach are illustrated on a batch crystallization process.

The paper presents a relay method for the identification of completely unknown processes for autotune purposes. It is an extension of a previous technique (ATV; Li, W.; et al. Ind. Eng. Chem. Res. 1991, 30, 1530), which assumed the delay of the process to be known. By means of a maximum of three relay tests, with additional delay, models with up to five parameters can be built. The proposed procedure does not present any convergence problem and is equivalent to the original one in terms of relative accuracy and duration of tests. The application of the identification procedure to a much wider set than in the original paper shows that the model obtained from the identification presents good accuracy in the high-frequency region, while some discrepancies of different nature may be present in the low frequency. When a suitable tuning method, which exploits this characteristic, is adopted, reasonably good closed-loop performance can be achieved for proportional-integral control in all cases. The low sensitivity to experimental errors and the simple implementation make the method interesting for application in industrial autotuners.

A primary requirement for the realization of FSA systems is the availability of a mathematical model for the actual process, and the ways to obtain this model may be broadly categorized into 2 methodologies: physical modelling techniques or process identification methods. The physical modelling approach seeks to derive the process model using fundamental laws governing the operations of the process, such as continuity and energy equations. Therefore, the parameters in the model have physical interpretations and they may sometimes be obtained from direct measurements on the process. Clearly, this modelling method requires a good and thorough understanding of the functioning of the entire process. However, even when the requirement can be satisfied, rarely can all parameters of the model be obtainable through direct measurements. Therefore, often physical modelling methods cannot be used alone and they are complemented with other modelling techniques.

A comparison is presented between two improved relay techniques, the Two Channel Relay and the ATV+ identification, recently introduced to obtain more information about the process and therefore to improve the closed loop performance. The techniques are briefly described and the comparison is stated in terms of duration of experimental tests versus achievable performance. From the analysis of a large number of cases, the ATV+ method shows to give better results, because a parametric model is identified and a more accurate tuning technique can be employed; the corresponding experimental times are about twice longer.

A technique for on-line identification and tuning is proposed to be used in the framework of a MIMO autotuning procedure. The proposed technique does not suffer from the risks of instability and the lack in performance of common tuning techniques in MIMO autotuning. Identification is accomplished through an extension of the well known ATV autotune identification method and requires only few additional tests in order to obtain some more knowledge about the process. The resulting model, which describes with good precision the process in a region of frequencies around the critical point, is then used for tuning: the integral time is found as a function of the model time constants and delay, while the gain is computed in order to give a desired value of the closed-loop resonance peak. Examples of application show that advantages over other proposed techniques can be retained for processes having different dynamic characteristics.

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 1998, Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 1998, Bu tezde gerçek zamanda bir sıcaklık sisteminin parametrelerinin belirlenmesi ele alınmıştır. Çalışmalarda servo ve regülator konrolü GMV Özuyarlamalı Konrol Ediciler ve PID Konrol Ediciler kullanılarak belirli referans değerleri etrafında yapılmıştır. Prosesin mertebesi Cohen ve Coon metodu kulanılarak elde edilmiştir ve bu değerler PID için doğrudan kullanılmış, diğeri içinse numerik hesaplama ile sürekli yenilenmiştir. Daha sonra değişik servo ve regülatör konrolü için kontrol edici performansı incelenmiştir., In this thesis, online process identification of a heating system is carried out. This is done by servo and regulatory control around certain set points using the GMV Self-Tuning Controller and a classical PID Controller. The process order is determined by Cohen and Coon method, and the parameters for the PID Controller are used directly, but for the other controller it is found by numerical estimation and updated continuously. Then the output performance of the controller is observed for different servo and regulatory changes., Yüksek Lisans, M.Sc.

Hydroxymethyfurfural (HMF) content and loss of available lysine were measured as indices of heat damage in various Spanish commercial milks (pasteurized, UHT sterilized and in-bottle sterilized milks) with similar processing dates. HMF level was determined by the traditional colorimetric procedure and by the reversed-phase HPLC method. Available lysine was measured by an alternative method with o-phthaldialdehyde as fluorescent marker. A significative relationship has been found between the HMF content and loss of available lysine and both are suitable for use as heat-induced indices. When loss of lysine (y) was expressed as percentage and HMF (x) as ¿m, the function of the correlation line was, y=0.47x¿0.089 (r=0.958, P

There is a growing need for ultrastable quartz oscillators operating under the special environmental conditions usually required in space program applications. This is the case for radiocommunications, navigation, precise positioning and scientific programs especially in the case of satellite applications. Under those conditions an evaluation program based on general rules ESA/SCC has been launched in 1992 for precise space evaluation of mini-BVA resonators. A special Process Identification Document (PID) has been established which includes fabrication processes and test procedures. 21 resonators have been fabricated according to the proposed PID. Then a test program has been defined and resonators have been space evaluated with interesting results which are discussed. Finally, it appeared that performances goals have been obtained. In conclusion special resonators can be fabricated by LCEP with guaranteed performance.

This paper gives an overview of identification techniques for use in model based predictive control (MPC) applications. Use of parametric and nonparametric models are discussed such as impulse- and step reponse models, statespace models, innovation form models and ARMAX/ARIMAX models. Prediction error methods for on-line and offline identification are presented, control relevant identification techniques based on multistep prediction error identification are presented and identifiability measures and test signal selection are discussed.

In recent years, multilayer feedforward neural networks have been used for chemical process identification [9]. One argument used repeatedly against neural network models is that they are “black-box” input/output models. The exact nature of the mapping from inputs to outputs is not easily understood. However, the advantage of neural network models is that they are able to predict process behavior accurately without any a priori knowledge of the process. Alternatives to input/output models are first principles (FP) models. The FP models describe the details of a process, but they are hard to develop, complex and difficult to solve. This paper discusses an approach to integrating FP models and neural network models, Integrated Neural Network (INN) models. Model mismatch between the FP model and the process is captured by the neural network. A polymer reactor system is chosen as a case study to demonstrate the technique.

Process identification is the subject of constructing models for certain utilitarian purposes from measured data. A first step in the total identification procedure is to determine a class of models in which the most suitable model is to be sought. In this chapter we will review some well known results on how to describe dynamic processes and signals using linear models.

Chemical treatment, for the purposes of this monograph, refers to the use of reagents to destroy or chemically modify target contaminants by means other than pyrolysis or combustion. The monograph addresses techniques used to chemically treat contaminated soils, groundwaters, surface waters, and, to a limited extent, concentrated contaminants. Chemical treatment is a means of converting hazardous constituents into less environmentally objectionable forms in order to meet treatment objectives.

In this chapter, we will solve the problem of multi-input multi-output (MIMO) process identification for robust control. We will show that all the steps of the two-step method developed in the previous chapter can be generalized for MIMO processes. For the sake of simplicity, we will restrict ourselves to open-loop experiments. In Section 8.1 the MIMO version of the asymptotic theory is presented. The identification procedure is presented in Section 8.2. In Section 8.3, two MIMO industrial processes will be identified using our method, and the results will be compared with those of the prediction methods. In Section 8.4 we will show that closed-loop identification can be transformed into two open-loop problems. Section 8.5 gives the conclusions.

Evaluation of the performance of a paper machine control system was carried out using statistical methods. A sequential approach to the evaluation and optimization of control systems was used to evaluate and optimize control system performance. Time series techniques were used to determine the potential for improved process control. Spectral analysis techniques and simple open-loop process identification experiments were used to find the cause of poor control system performance. Significant improvements in control system performance were realized.

Temperature-controlled laser transformation hardening is going to be established in industrial manufacturing of workpieces with complex geometries to ensure and increase the quality of the product as well as to reduce the influence of disturbances. The common structure of every process control is a feed-back loop which is built up here by a pyrometer, a controller and a flexible laser handling system. Specific problems occur in temperature-controlled transformation hardening of coated workpieces as the surface temperature ist not only determined by the process parameters and the thermophysical properties of the base material but also by those of the coating. An adaptive temperature control system (ATC) which estimates and controls the workpiece temperature underneath a coating is presented in this paper. The concept of the process identification and the control algorithm will be explained and experimental results will be reported.

A software package for process identification will be described with emphasis on its application to laboratory pilot plants and full scale industrial production units. Methods for off-line parameter estimation as well as real-time recursive versions are evaluated. The recursive identification methods are extensively used in connection with control algorithms. The resulting adaptive controllers are also dealt with in this paper.

The necessity to find solutions for some practical aspects of process identification by statistical methods - spectral analysis, chiefly - made us approach problems of identification from disturbed measurements. The following two aspects are investigated: (1) influence of measurement disturbances on statistical functions estimation: (2) influence exert on identification results by the errors of statistical functions estimation from disturbed measurements.

A selftuning PID controller used as a tuning aid in a process control system is described with respect to the underlying methods and the tuning procedure implemented on the operator interface. The tuning procedure has been applied successfully to the control loops of several industrial processes. Results are presented for the tuning of 4 control loops of a steam generator. Comparisons are made with respect to the control performance between standard PID parameter settings and the optimized parameter settings achieved with the PID selftuner.

An identification method is described which first identifies a linear nonparametric model (crosscorrelation function, impulse response) by correlation analysis and then estimates the parameters of a parametric model (discrete transfer function) and also includes a method for the detection of the model order and the time delay. The performance, the computational expense and the overall reliability of this method is compared with five other identification methods. This two-step identification method, which can be applied off-line or on-line, is especially suited to identification by process computers, since it has the properties: Little a priori knowledge about the structure of the process model; very short computation time; small computer storage; no initial values of matrices and parameters are necessary and no divergence is possible for the on-line version. Results of an on-line identification of an industrial process with a process computer are shown.