Samopodešavajuće prediktivno funkcionalno upravljanje temperaturom egzotermičkog šaržnog reaktora

In this paper we study a self-adaptive predictive functional control algorithm as an approach to control of temperature in an exothermic batch reactor. The batch reactor is situated in a pharmaceutical company in Slovenia and is used in the production of medicines. Due to mixed discrete and continuous inputs the reactor is considered as a hybrid system. The model of the reactor used for the simulation experiment is explained in the paper. Next, we assumed an exothermic chemical reaction that is carried out in the reactor core. The dynamics of the chemical reaction that comply with the Arrhenius relation have been well documented in the literature and are also summarized in the paper. Furthermore, the online recursive least-squares identification of the process parameters and the self-adaptive predictive functional control algorithm are thoroughly explained. We tested the proposed approach on the batch reactor simulation example that included the exothermic chemical reaction kinetic model. The results suggest that such implementation meets the control demands, despite the strongly exothermic nature of the chemical reaction. The reference is suitably tracked, which results in a shorter overall batchtime. In addition, there is no overshoot of the controlled variable (temperature in the reactor core), which yields a higher-quality production. Finally, by introducing a suitable discrete switching logic in order to deal with the hybrid nature of the batch reactor, we were able to reduce switching of the on/off valves to minimum and therefore relieve the wear-out of the actuators as well as reduce the energy consumption needed for control.U članku se analizira samopodešavajući algoritam prediktivnog funkcionalnog upravljanja kao pristup upravljanju temperaturom egzotermičkog šaržnog reaktora. Šaržni se reaktor nalazi u jednoj slovenskoj farmaceutskoj tvrtki gdje se koristi za proizvodnju medikamenata. Budući da su ulazi u rektor i kontinuirani i diskretni, reaktor je promatran kao hibridni sustav. U članku je opisan model reaktora korišten za simulacije. Nadalje, pretpostavljeno je da se u jezgri reaktora odvija egzotermička reakcija. Opis dinamike kemijske reakcije Arrheniusovim jednadžbama dobro je dokumentiran u literaturi, pa je u članku dan samo kratki pregled. Posebno detaljno opisana je metoda najmanjih kvadrata za procjenu parametara modela te samopodešavajući agoritam prediktivnog funkcionalnog upravljanja. Predloženi pristup upravljanju provjeren je simulacijom na šaržnom reaktoru koji uključuje kinetički model egzoterničke kemijske reakcije. Simulacijski rezultati ukazuju da predloženo upravljanje ispunjava tražene zahtjeve, unatoč jakoj egzotermičkoj naravi kemijske reakcije. Zadane su reference dobro praćene, što rezultira skraćenjem trajanja šaržnog procesa. Osim toga, nepostojanje nadvišenja u temperaturi jezgre reaktora osigurava veću kakvoću proizvodnje. Na koncu, uvođenjem prikladne logike prekapčanja za prilagodbu hibridnoj naravi šaržnog reaktora moguće je značajno smanjiti prekapčanje dvopoložajnih ventila što ima za posljedicu smanjenje njihova trošenja i uštedu u potrošnji energije

An automated indoor localization system for online Bluetooth signal strength modeling using visual-inertial SLAM

Indoor localization is becoming increasingly important but is not yet widespread because installing the necessary infrastructure is often time-consuming and labor-intensive, which drives up the price. This paper presents an automated indoor localization system that combines all the necessary components to realize low-cost Bluetooth localization with the least data acquisition and network configuration overhead. The proposed system incorporates a sophisticated visual-inertial localization algorithm for a fully automated collection of Bluetooth signal strength data. A suitable collection of measurements can be quickly and easily performed, clearly defining which part of the space is not yet well covered by measurements. The obtained measurements, which can also be collected via the crowdsourcing approach, are used within a constrained nonlinear optimization algorithm. The latter is implemented on a smartphone and allows the online determination of the beacons’ locations and the construction of path loss models, which are validated in real-time using the particle swarm localization algorithm. The proposed system represents an advanced innovation as the application user can quickly find out when there are enough data collected for the expected radiolocation accuracy. In this way, radiolocation becomes much less time-consuming and labor-intensive as the configuration time is reduced by more than half. The experiment results show that the proposed system achieves a good trade-off in terms of network setup complexity and localization accuracy. The developed system for automated data acquisition and online modeling on a smartphone has proved to be very useful, as it can significantly simplify and speed up the installation of the Bluetooth network, especially in wide-area facilities

Modelling and internal fuzzy model power control of a Francis water turbine

This paper presents dynamic modelling of a Francis turbine with a surge tank and the control of a hydro power plant (HPP). Non-linear and linear models include technical parameters and show high similarity to measurement data. Turbine power control with an internal model control (IMC) is proposed, based on a turbine fuzzy model. Considering appropriate control responses in the entire area of turbine power, the model parameters of the process are determined from a fuzzy model, which are further included in the internal model controller. The results are compared to a proportional-integral (PI) controller tuned with an integral absolute error (IAE) objective function, and show an improved response of internal model control

Predictive Approaches to Control of Complex Systems

A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems

Halfway to automated feeding of chinese hamster ovary cells

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry

Evolving Gustafson-kessel Possibilistic c-Means Clustering

AbstractThis paper presents an idea of evolving Gustafson-Kessel possibilistic c-means clustering (eGKPCM). This approach is extension of well known possiblilistic c-means clustering (PCM) which was proposed to address the drawbacks associated with the constrained membership functions used in fuzzy c-means algorithms (FCM). The idea of possiblistic clustering is ap- pealing when the data samples are highly noisy. The extension to Gustafson-Kessel possibilistic clustering enables us to deal with the clusters of different shapes and the evolving structure enables us to cope with the data structures which vary during the time. The evolving nature of the algorithm makes it also appropriate for dealing with big-data problems. The proposed approach is shown on a simple classification problem of unlabelled data

Modelling and Internal Fuzzy Model Power Control of a Francis Water Turbine

This paper presents dynamic modelling of a Francis turbine with a surge tank and the control of a hydro power plant (HPP). Non-linear and linear models include technical parameters and show high similarity to measurement data. Turbine power control with an internal model control (IMC) is proposed, based on a turbine fuzzy model. Considering appropriate control responses in the entire area of turbine power, the model parameters of the process are determined from a fuzzy model, which are further included in the internal model controller. The results are compared to a proportional-integral (PI) controller tuned with an integral absolute error (IAE) objective function, and show an improved response of internal model control

An overview on evolving systems and learning from stream data

Evolving systems unfolds from the interaction and cooperation between systems with adaptive structures, and recursive methods of machine learning. They construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components that assemble the system structure can be chosen, being rules, trees, neurons, and nodes of graphs amongst the most prominent. Evolving systems relate mainly with time-varying environments, and processing of nonstationary data using computationally efficient recursive algorithms. They are particularly appropriate for online, real-time applications, and dynamically changing situations or operating conditions. This paper gives an overview of evolving systems with focus on system components, learning algorithms, and application examples. The purpose is to introduce the main ideas and some state-of-the-art methods of the area as well as to guide the reader to the essential literature, main methodological frameworks, and their foundations11181198CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ305906/2014-3This work was supported by Instituto Serrapilheira (Grant No. Serra-1812-26777), Javna Agencija za Raziskovalno Dejavnost RS (Grant No. P2-0219) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (Grant No. 305906/2014-3

Depth-image segmentation based on evolving principles for 3D sensing of structured indoor environments

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner

Soft sensor of bath temperature in an electric arc furnace based on a data-driven Takagi–Sugeno fuzzy model

Electric arc furnaces (EAFs) are intended for the recycling of steel scrap. One of the more important variables in the recycling process is the tapping temperature of the steel. Due to the nature of the process, continuous measurement of the melt temperature is complicated and requires sophisticated measuring equipmenttherefore, for most EAFs, separate temperature samples are taken several times before the melt is tapped, to verify whether the melt temperature is within the prescribed range. The measurements are obtained using disposable probeswhen measurement is performed, the furnace must be switched off, leading to increased tap-to-tap time, unnecessary energy losses, and consequently, lower efficiency. The following paper presents a novel approach to EAF bath temperature estimation using a fuzzy model soft sensor obtained using Gustafson–Kessel input data clustering and particle swarm optimization of model parameters. The model uses the first temperature measurement as an initial condition, and measurements of the necessary EAF inputs to estimate continuously the bath temperature throughout the refining stage of the recycling process. The results have shown that the prediction accuracy of the proposed model is very high and that it fulfils the required tolerance band. The model is intended for parallel implementation in the EAF process, with the aim of achieving fewer temperature measurements, shorter tap-to-tap times, and decreased energy losses. Furthermore, if information about bath temperature is accessible in a continuous manner, operators can adjust the control of the EAF to achieve optimal tapping temperature and thus higher EAF efficiency