Robust evolving cloud-based controller in normalized data space for heat-exchanger plant

This paper presents an improved version and a modification of Robust Evolving Cloud-based Controller (RECCo). The first modification is normalization of data space in RECCo. As a consequence, some of the evolving and adaptation parameters become independent of the range of the process output signal. Thus the controller tuning is simplified which makes the approach more appealing for the use in practical applications. The data space normalization is general and is used with Euclidean norm, but other distance metrics could also be used. Beside the normalization new adaptation scheme of the controller gain is proposed which improves the control performance in the case of a negative initial error in starting phase of the evolving process. At the end, different simulation scenarios are tested and analyzed for further practical implementation of the Cloud-based controller into real environments. For that reason a detail simulation study of a plate heat exchanger is performed and different scenarios were analyzed

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. It starts with zero fuzzy rules (clouds) in the rule base and evolves its structure while performing the control of the plant. For the consequent part of RECCo PID-type controller is used and the parameters are adapted in an online manner. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). Moreover, in this article we propose a normalization of the cloud (data) space and an improved adaptation law of the controller. Due to the normalization some of the evolving parameters can be fixed while the new adaptation law improves the performance of the controller in the starting phase of the process control. To assess the performance of the RECCo algorithm, firstly a comparison study with classical PID controller was performed on a model of a plate heat-exchanger (PHE). Tuning the PID parameters was done using three different techniques (Ziegler–Nichols, Cohen–Coon and pole placement). Furthermore, a practical implementation of the RECCo controller for a real PHE plant is presented. The PHE system has nonlinear static characteristic and a time delay. Additionally, the real sensor's and actuator's limitations represent a serious problem from the control point of view. Besides this, the RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner

Analysis of adaptation law of the robust evolving cloud-based controller

In this paper we propose a performance analysis of the robust evolving cloud-based controller (RECCo) according to the different initial scenarios. RECCo is a controller based on fuzzy rule-based (FRB) systems with non-parametric antecedent part and PID type consequent part. Moreover, the controller structure (the fuzzy rules and the membership function) is created in online manner from the data stream. The advantage of the RECCo controller is that do not require any a priory knowledge of the controlled system. The algorithm starts with zero fuzzy rules (zero data clouds) and evolves/learns during the process control. Also the PID parameters of the controller are initialed with zeros and are adapted in online manner. According to the zero initialization of the parameters the new adaptation law is proposed in this article to solve the problems in the starting phase of the process control. Several initial scenarios were theoretically propagated and experimentally tested on the model of a heat-exchanger plant. These experiments prove that the proposed adaptation law improve the performance of the RECCo control algorithm in the starting phase

Robust Evolving Cloud-based Controller (ReCCo)

This paper presents an autonomous Robust Evolving Cloud-based Controller (RECCo). The control algorithm is a fuzzy type with non-parametric (cloud-based) antecedent part and adaptive PID-R consequent part. The procedure starts with zero clouds (fuzzy rules) and the structure evolves during performing the process control. The PID-R parameters of the first cloud are initialized with zeros and furthermore, they are adapted on-line with a stable adaptation mechanism based on Lyapunov approach. The RECCo controller does not require any mathematical model of the controlled process but just basic information such as input and output range and the estimated value of the dominant time constant. Due to the problem space normalization the design parameters are fixed. The proposed controller with the same initial design parameters was tested on two different simulation examples. The experimental results show the convergence of the adaptive parameters and the effectiveness of the proposed algorithm

Evolving cloud-based system for the recognition of drivers' actions

This paper presents an evolving cloud-based algorithm for the recognition of drivers' actions. The general idea is to detect different manoeuvres by processing the standard signals that are usually measured in a car, such as the speed, the revolutions, the angle of the steering wheel, the position of the pedals, and others, without additional intelligent sensors. The primary goal of this investigation is to propose a concept that can be used to recognise various driver actions. All experiments are performed on a realistic car simulator. The data acquired from the simulator are pre-processed and then used in the evolving cloud-based algorithm to detect the basic elementary actions, which are then combined in a prescribed sequence to create tasks. Finally, the sequences of different tasks form the most complex action, which is called a manoeuvre. As shown in this paper, the evolving cloud-based algorithm can be very efficiently used to recognise the complex driver's action from raw signals obtained by typical car sensors. (C) 2017 Elsevier Ltd. All rights reserved.This work has been supported by the Program Chair of Excellence of Universidad Carlos III de Madrid and Bank of Santander and the Spanish Ministry of Economy, Industry and Competitiveness, projects TRA2015-63708-R and TRA2016-78886-C3-1-R

EVOLVING SYSTEMS IN PROCESS MONITORING AND CONTROL

V pričujoči doktorski disertaciji smo raziskovali različna področja uporabe samorazvijajočih se sistemov, kot so vodenje, identikacija in spremljanje procesov. Samorazvijajoči se sistemi, ki jih obravnavamo v disertaciji, temeljijo na posplošenem mehkem modelu AnYa (po priimkih avtorjev Angelov in Yager), ki se razlikuje od klasičnih mehkih modelov (Mamdani in Takagi-Sugeno) v načinu deniranja strukture modela. Namesto vnaprej deniranih mehkih pravil (Gaussovo, trikotno, lingvistično itn.), model AnYa na podlagi sprotno sprejetih podatkov tvori mehka pravila v obliki oblakov podatkov (ang. data clouds). Z vsakim novim prejetim podatkom se struktura in parametri modela prilagajajo novim spremenjenim stanjem v procesu. To nam omogoča implementacijo različnih algoritmov sprotnega vodenja, identikacije ali spremljanja dinamičnih procesov. Poleg implementacije teh algoritmov smo v doktorski disertaciji obravnavali tudi mehanizme samorazvijanja modelov za dodajanje novih in odstranjevanje nepomembnih oblakov. Prav tako smo iskali načine, kako preprečiti dodajanje oblakov na osnovi osamelcev. V disertaciji smo predstavili robusten samorazvijajoči se adaptivni mehki regulator (ang. robust evolving cloud-based controller, RECCo). Regulator je sestavljen iz dveh glavnih delov: samorazvijajoča se struktura modela (z mehanizmom za dodajanje novih oblakov na podlagi lokalne gostote podatkov) in sprotna adaptacija parametrov lokalnih regulatorjev (na podlagi gradienta kriterijske funkcije). Mehanizem samorazvijanja modela skrbi za zaznavanje nelinearnih področij v procesu, kar pomeni, da se parametri lokalnih regulatorjev prilagajajo delovni točki procesa. Z normiranjem podatkovnega prostora smo dosegli enostavnejše nastavljanje začetnih parametrov regulatorja. Podali smo tudi smernice, kako nastaviti oziroma izračunati začetne vrednosti parametrov regulatorja. V doktorski disertaciji smo prikazali nekaj primerov uporabe RECCo-regulatorja na simuliranih in realnih napravah. Vodenje na simuliranih procesih smo izvedli na modelu toplotnega izmenjevalnika in na modelu distribuiranega sistema sončnih kolektorjev. Uporaba vodenja na realnih napravah pa je bila izvedena pri regulaciji temperature na toplotnem izmenjevalniku in regulaciji nivoja na sistemu dveh povezanih tankov. Mehanizem samorazvijanja na osnovi oblakov podatkov smo vpeljali tudi v mehki prediktivno funkcijski regulator (ang. fuzzy cloud-based predictive func- tional controller, FCPFC). Za delovanje tega regulatorja potrebujemo model procesa, ki ga želimo voditi. S tem namenom smo združili samorazvijajoči se model z rekurzivno metodo najmanjših kvadratov. Na ta način lahko identiciramo dinamičen model procesa, ki je potem del prediktivnega regulatorja. Model uporabimo za predikcijo reguliranega signala na vnaprej določenem horizontu in nato določimo še regulirni signal, ki minimizira razliko med izhodnim in referen čnim/želenim signalom. Taksen pristop je primeren za regulacijo nelinearnih dinamičnih procesov. Delovanje predlaganega prediktivnega regulatorja FCPFC smo preizkusili na modelu reaktorja z neprekinjenim mešanjem (ang. continu- ous stirred tank reaktor, CSTR). Dobljene rezultate smo primerjali z RECCoregulatorjem. V nadaljevanju smo predlagali in preizkusili samorazvijajoči se model na osnovi oblakov za identikacijo dinamičnih sistemov. V tem primeru smo raziskali različne mehanizme dodajanja in odstranjevanja oblakov in njihov vpliv na učinkovitost celotne metode. Predlagano metodo smo preizkusili na dveh različnih primerih. Prvi primer je model kemičnega procesa Tennessee Eastman, ki ima zelo kompleksno strukturo in dinamiko. Iz tega modela smo pridobili simulirane podatke ter poskušali pridobiti modele kazalnikov proizvodnje učinkovitosti. Rezultate smo primerjali z metodo eFuMo ter z nevronskimi mrežami. Drugi primer je bil realen sistem hladilne postaje, ki obratuje v enem od podjetij v Sloveniji. Pridobljene podatke smo prav tako uporabili za identi- kacijo dinamičnih modelov nekaj ključnih kazalnikov proizvodnje. Te modele smo naknadno uporabili za nadzorovano in prediktivno preklapljanje hladilnih agregatov, ki so ključni elementi celotnega sistema. Izkazalo se je, da z uporabo modelov lahko preprečimo nepotrebna preklapljanja agregatov in s tem omogočimo boljšo učinkovitost celotnega sistema. V zadnjem delu smo samorazvijajoči se model uporabili kot orodje za spremljanje procesov. Z uporabo mehanizmov za dodajanje novih oblakov lahko deniramo področje procesa, ki opisuje normalno stanje delovanja, in področje, ki označuje napako na procesu. Nato lahko z izračunom lokalnih gostot za vsak podatek posebej določimo ali predstavlja napako oziroma normalno delovanje. Predlagali smo tudi izračun delnih lokalnih gostot z upoštevanjem najbolj vplivnih komponent. Delovanje metode smo preizkusili na področju zaznavanja napak na sistemu Tennessee Eastman. Rezultate smo primerjali z nekaj znanimi metodami za zaznavanje napak na procesih, kot so PCA (ang. principal compo- nent analysis), ICA (ang. independent component analysis) in FDA (ang. sher discriminate analysis). Rezultati metode so primerljivi in dosegajo podobno natan čnost, kot že uveljavljene metode na tem področju. Na koncu smo samorazvijajoči se model razdelili na več hierarhičnih nivojev z namenom zaznavanja manevrov pri voznikih osebnih avtomobilov, kot so prehitevanje, zaviranje, ustavljanje in podobno. Metoda uporablja le osnovne senzorje (in ne naprednih, kot so kamere, laserji itd.), ki so del standardne opreme osebnih avtomobilov. Izkazalo se je, da predlagani hierarhični koncept od spodaj navzgor (od manj do bolj kompleksnih akcij) v kombinaciji s samorazvijajočim se modelom uspešno zaznava in ločuje med različnimi manevri pri voznikih.In the doctoral thesis the usage of evolving systems on dierent elds was investigated, namely the eld of control, system identication and process monitoring. The evolving systems in the current thesis are based on the simplied fuzzy model AnYa which diers from the classical ones (such as Mamdani and Takagi-Sugeno) in the way how the model structure is dened. The AnYa model uses data clouds in the antecedent part of the fuzzy system instead of predened fuzzy rules (Gaussian, triangular, trapezoid etc.) in the classical models. Therefore, the structure of the AnYa could be adapted with each data point received in online manner. This leads to easy implementation of advanced on-line methods for control, identication or monitoring of dynamic processes. Beside this we have investigated dierent evolving mechanisms for adding new rules/clouds and for removing less important ones. Firstly we have presented a robust evolving cloud-based controller RECCo for nonlinear processes. The controller consists of two main parts: evolving mechanism (adding new clouds according to the local density of the data) and adaptive law of the local controllers\u27 parameters (based on gradient descent method). The evolving part takes care of the controlled process nonlinearity and the adaptive law adjusts to the current operating point. We proposed a normalization of the data space which simplies the process of setting the initial parameters. Also some instructions for setting/calculating the initial parameters are given. In the dissertation we show some practical examples of using the controller on simulated and real processes. For the simulated examples we used a model of heat exchanger and a model of distributed solar collector eld. Moreover a real plant of heat exchanger and two tank plant were used for temperature and level control, respectively. Based on the evolving model we also introduce a fuzzy cloud-based predictive functional controller (FCPFC). This controller requires a model of the controlled process. With this purpose we joined the evolving mechanisms with the recursive weighted least square method. Actually we developed a tool for identication of dynamic process model. The model is further used for the controlled signal prediction in a certain horizon and then the control signal is chosen to minimize the error between the predicted and the desired value of the signal. This approach is suitable for controlling nonlinear dynamic processes. The eciency of the proposed controller FCPFC was tested on Continuous Stirred Tank Reactor, CSTR. The results were also compared with the RECCo controller. In the second part of the doctoral thesis we proposed an evolving method for dynamic process identication. Actually the recursive identication method was already tackled in the previous part when FCPFC controller was proposed. In this part we additionally investigated dierent mechanisms for adding and removing data clouds (fuzzy rules) and their impact on the overall identi cation method eciency. The proposed method was tested on two dierent examples. The rst example was Tennessee Eastman process which has really complex structure with dynamic behavior. We acquired the necessary data from the model and then we identied the models of the production Performance Indicators (pPI). The obtained results were compared to the established methods eFuMo and neural networks. The second example was a real process of water chiller plant (WCP). The data were acquired directly from the plant and were used for model identication of the key indicators. The models were further used for process monitoring and predictive operating with the chillers, which are the key elements of the whole system. It has been shown that using the acquired models we can prevent unnecessary switching of the chillers which leads to more ecient operation of the system. In the last part of the thesis we investigated the usage of the evolving system for process monitoring and fault detection purposes. Using the evolving model we can dene the part of data space which describe normal process operation and faults. According to the local density we can easily determine if each data sample is a fault or not. Moreover we proposed a partial data density calculation which takes into account only the most in uential components. The eciency of the method was tested on the Tennessee Eastman process. The results were compared with well established methods on the eld such as: PCA (principal component analysis), ICA (independent component analysis) and FDA (sher discriminate analysis). With the comparison analysis of the results we show that the proposed evolving method is comparable with the established ones. At the end we developed a hierarchical evolving model for car-driver behavior detection. The method uses the basic sensors (no cameras, radars etc.) in the car which are part of standard equipment of a modern car. We show that with the proposed hierarchical concept and the evolving model we can eciently detect dierent maneuvers (such as overtaking, stopping, breaking, etc.) and can dierentiate between them

Spectrophotometric study of the protonation processes of some indole derivatives in sulfuric acid

The protonation of 3-methylindole, D-tryptophan, 3-formylindole, 3-acetylindole and indolyl-2-carboxylic acid in sulfuric acid media was studied by UV spectro-scopy. The measurement of the absorbance at four selected wavelengths enabled the calculation of the corresponding molar absorptivities. The results were used to calculate the pKa value of the protonated form of the indole derivatives by the Hammett Method. The Hammett postulate (the slope of the plot log [c(BH+)/c(B)] vs. H should be equal to 1) was tested. The dissociation constants and solvent parameter m* were also obtained by applying the Excess Acidity Method. The position of the additional protons in the protonated compounds is discussed

Data-Driven Modelling and Optimization of Energy Consumption in EAF

In the steel industry, the optimization of production processes has become increasingly important in recent years. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data from more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analysed. Information on the consumed energy in each step of the electric arc process is essential to increase the efficiency of the EAF. In the paper, four different modelling approaches for predicting electrical energy consumption during EAF operation are presented: linear regression, k-NN modelling, evolving and conventional fuzzy modelling. In the learning phase, from a set of more than ten regressors, only those that have the greatest impact on energy consumption were selected. The obtained models that can accurately predict the energy consumption are used to determine the optimal duration of the transformer profile during melting. The models can predict the optimal energy consumption by selecting pre-processed training data, where the main steps are to find and remove outlier batches with the highest energy consumption and identify the influencing variables that contribute most to the increased energy consumption. It should be emphasised that the electrical energy consumption was too high in most batches only because the melting time was unnecessarily prolonged. Using the proposed models, EAF operators can obtain information on the estimated energy consumption before batch processing depending on the scrap weight in each basket and the added additives, as well as information on the optimal melting time for a given EAF batch. All models were validated and compared using 30% of all data, with the fuzzy model in particular providing accurate prediction results. It is expected that the use of the developed models will lead to a reduction in energy consumption as well as an increase in EAF efficiency

Optimal rule-based granular systems from data streams

We introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use α-level sets over Gaussian membership functions to set model granularity and operate with hyperrectangular forms of granules in nonstationary environments. The resulting rule-based systems are formed in a formal and systematic fashion. They can be useful in time series modeling, dynamic system identification, predictive analytics, and adaptive control. Precise estimates and enclosures are given by linear piecewise and inclusion functions related to optimal granular mappings283583596CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ305906/2014-3The work of D. Leite was supported by a grant from the Serrapilheira Institute. The work of I. Skrjanc was supoorted by the Slovenian Research Agency, research program P2-0219, Modeling, simulation and control. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for Grant 305906/2014-

Data-Driven Modelling and Optimization of Energy Consumption in EAF

In the steel industry, the optimization of production processes has become increasingly important in recent years. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data from more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analysed. Information on the consumed energy in each step of the electric arc process is essential to increase the efficiency of the EAF. In the paper, four different modelling approaches for predicting electrical energy consumption during EAF operation are presented: linear regression, k-NN modelling, evolving and conventional fuzzy modelling. In the learning phase, from a set of more than ten regressors, only those that have the greatest impact on energy consumption were selected. The obtained models that can accurately predict the energy consumption are used to determine the optimal duration of the transformer profile during melting. The models can predict the optimal energy consumption by selecting pre-processed training data, where the main steps are to find and remove outlier batches with the highest energy consumption and identify the influencing variables that contribute most to the increased energy consumption. It should be emphasised that the electrical energy consumption was too high in most batches only because the melting time was unnecessarily prolonged. Using the proposed models, EAF operators can obtain information on the estimated energy consumption before batch processing depending on the scrap weight in each basket and the added additives, as well as information on the optimal melting time for a given EAF batch. All models were validated and compared using 30% of all data, with the fuzzy model in particular providing accurate prediction results. It is expected that the use of the developed models will lead to a reduction in energy consumption as well as an increase in EAF efficiency