•A novel method for adaptive soft sensor development is proposed.•Input variable selection procedure based on mutual information is proposed.•Application to Tennessee Eastman process and real ...industrial processes.
Linear models can be inappropriate when dealing with nonlinear and multimode processes, leading to a soft sensor with poor performance. Due to time-varying process behaviour it is necessary to derive and implement some kind of adaptation mechanism in order to keep the soft sensor performance at a desired level. Therefore, an adaptation mechanism for a soft sensor based on a mixture of Gaussian process regression models is proposed in this paper. A procedure for input variable selection based on mutual information is also presented. This procedure selects the most important input variables for output variable prediction, thus simplifying model development and adaptation. Apart from online prediction of the difficult-to-measure variable, this soft sensor can be used for adaptive process monitoring. The efficiency of the proposed method is benchmarked with the commonly applied recursive PLS and recursive PCA method on the Tennessee Eastman process and two real industrial examples.
Increased use of energy in buildings and HVAC systems requires advanced control schemes like model-based control to improve energy efficiency, which in turn requires accurate thermodynamic models of ...buildings. The Resistive-Capacitive (RC) method is a popular and versatile approach for thermal modeling of buildings. Despite this, it is not easy to find practical solutions of implementation of the RC method. It is the goal of this paper to clarify the RC method and demonstrate simple implementation of this method, especially for multi-zone buildings, which have more potential for energy savings from use of model-based control. This paper provides two contributions. First is a detailed explanation of the RC method, focusing on its use for developing a structure of a model and first-principles approach for estimation of parameters of a model. Second is a demonstration of an algorithm that enables automatic development of the structure of a model from basic information about a building (layout, construction elements) and its combination with data-based parameter estimation. Use of the algorithm is presented with a case-study on industrial multi-zone building, for which such a grey-box model is developed and analyzed. The resulting model is rapidly developed and used in a simulation with the measured data. The outputs of the model are compared with the measured temperatures and they show good fit.
•The method for stream water temperature prediction is proposed.•The method is based on Gaussian process regression model.•Input variable selection is based on mutual information.
The prediction of ...stream water temperature presents an interesting topic since the water temperature has a significant ecological and economical role, such as in species distribution, fishery, industry and agriculture water exploitation. The prediction of stream water temperature is usually based on appropriate mathematical model and measurements of different atmospheric factors. In this paper, a probabilistic approach to daily mean water temperature prediction is proposed. The resulting model is a combination of two Gaussian process regression models where the first model describes the long-term component of water temperature and the other model describes the short-term variations in water temperature. The proposed approach is developed even further by modeling the short-term variations with multiple Gaussian process regression models instead with a single one. Apart from that, variable selection procedure based on mutual information is presented which is suitable for input variable selection when nonlinear models for stream water prediction are developed. The proposed approach is compared with traditional modeling approaches on the measurements obtained on the Drava river in Croatia. The presented methodology can be used as a basis of the predictive tools for water resource managers.
Industrial automation of the production process is based on the fusion of a CNC machine and an industrial robot. The industry of today requires skilled professionals and educators. Special attention ...is to be paid to the testing of the components and system operation and the maintenance of the system. Robots and automation are omnipresent nowadays and have also taken a significant role in education. The research presented here aims to overhaul the scaled model of the cup-filling machine to make it operate fully automated. The parts and subsystems of the cup-filling machine are explained in detail and their operation was tested. The cup-filling machine is fully automated using a programmable logic controller (PLC) SIEMENS S7 300. The machine can recognize two cup sizes and fills both types without overspilling. Filled cups are transported over a conveyor belt and classified according to their sizes PLCs have mainly replaced relays in industrial automation, bearing in mind that this way, scale-up is much more feasible, and alteration of control is done in PLC program code. This also has contributed to better maintenance and operation verification.
There has been an increased use of soft-sensors in process industry in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to hardware sensors. ...Since process variables, which are concerned with final product quality, cannot always be measured by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a soft-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or replacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are generally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plant data, taken from the process database, are available. In this paper the most popular methods for plant data-based modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a short survey of the most important papers, tips about choosing the appropriate methodology for process model building are also provided.
Problemi s upravljanjem mnogih procesa u industriji vezani su s nemogućnošću on-line mjerenja nekih važnih procesnih veličina. Ovaj se problem može u značajnoj mjeri riješiti estimacijom ovih ...teško-mjerljivih procesnih veličina. Estimator je pri tome odgovarajući matematički model procesa koji na temelju informacije o ostalim (lako-mjerljivim) procesnim veličinama procjenjuje trenutni iznos teško-mjerljive veličine. Budući da su procesi po prirodi promjenjivi, točnost estimacije zasnovane na modelu procesa izgra.enog na starim podacima u pravilu opada s vremenom. Kako bi se ovo izbjeglo, parametre modela procesa je potrebno kontinuirano prepodešavati kako bi model što bolje opisivao (trenutno) vladanje procesa. Ovisno o tipu matematičkog modela, za prepodešavanje njegovih parametara na raspolaganju je više metoda. Kao osnova estimatora teško-mjerljive veličine u radu se koristi PLSR model procesa, dok se njegovi parametri prepodešavaju na više načina – metodom pomičnog prozora, rekurzivnim NIPALS algoritmom, rekurzivnim kernel algoritmom te Just-in-Time Learning metodom. Svojstva navedenih metoda adaptacije PLSR modela procesa ispitana su na odabranom primjeru. Nadalje, metode adaptacije su analizirane i s obzirom na računalnu i memorijsku zahtjevnost.
There exist many problems regarding process control in the process industry since some of the important variables cannot be measured online. This problem can be significantly solved by estimating ...these difficult-to-measure process variables. In doing so, the estimator is in fact an appropriate mathematical model of the process which, based on information about easy-to-measure process variables, estimates the current value of the difficult- to-measure variable. Since processes are usually time-varying, the precision of the estimation based on the process model which is built on old data is decreasing over time. To avoid estimator accuracy degradation, model parameters should be continuously updated in order to track process behavior. There are a couple of methods available for updating model parameters depending on the type of process model. In this paper, PLSR process model is chosen as the basis of the difficult-to-measure process variable estimator while its parameters are updated in several ways-by the moving window method, recursive NIPALS algorithm, recursive kernel algorithm and Just-in-Time learning algorithm. Properties of these adaptive methods are explored on a simulated example. Additionally, the methods are analyzed in terms of computational load and memory requirements.
Image based meteor detection and path estimation Novoselnik, Filip; Grbić, Ratko; Slišković, Dražen
2016 International Conference on Smart Systems and Technologies (SST),
2016-Oct.
Conference Proceeding
Reliable detection of meteor streaks in video recordings is of great importance for numerous scientific and educational purposes. In this paper, a novel method for meteor detection and path ...estimation in stacked images is proposed. The method is based on a four stage pipeline. Additionally, a dataset is created which can be used for evaluation of different meteor detection algorithms. The proposed method has a high efficiency with 90% accuracy and 95% sensitivity regarding image classification in terms of meteor presence in image. The proposed method achieves a high percentage (~93%) in meteor streak localization and path estimation as well.
Energy efficiency is becoming more important issue, especially in buildings sector where 15-20% of total energy in developed countries is used. One course toward higher efficiency is to improve HVAC ...(Heating, Ventilation and Air Conditioning) systems in building, especially by implementation of advanced control methods. Efficiency of these control methods (especially Model Predictive Control) significantly depends on quality of thermal models, where quality can be defined by accuracy and complexity. This paper investigates relationships between accuracy and complexity in thermal models produced with RC (Resistance-Capacitance) equivalent method, with parameters improved using optimization and estimation from data. Four experiments using simulation data regarding importance of selection of error function, selection of training data, selection of model complexity and reduction of complexity are conducted and explained.
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