Application of Artificial Neural Networks for Process Identification and Control

Pri razvoju inteligentnih sustava u posljednjih dvadesetak godina ostvarena su brojna unapređenja inspirirana biološkim neuronskim sustavom. Istraživači s različitih znanstvenih područja kreirali su i primijenili umjetne neuronske mreže za rješavanje niza zadataka - od prepoznavanja uzoraka, predviđanja, dijagnosticiranja stanja, softverskih senzora, modeliranja i identificiranja, vođenja i optimiranja procesa itd. Umjetne neuronske mreže pokazale su se korisnim u primjeni kod složenih kemijskih i biokemijskih procesa gdje standardnim metodama nije moguće uspješno modelirati procese i dobivene modele primijeniti za vođenje procesa. Danas, zahvaljujući intenzivnom razvoju teorije i praktične primjene neuronskih mreža, stoje na raspolaganju brojne strukture i algoritmi. U radu je dan pregled primjene neuronskih mreža s težištem na identificiranju i vođenju procesa na polju kemijskog inženjerstva. Istaknuti su primjeri primjene kod prediktivnog, inverznog i prilagodljivog vođenja procesa.During the development of intelligent systems inspired by biological neural system, in the last two decades the researchers from various scientific fields have created neural networks for solving a series of problems from pattern recognition, prediction, diagnostic, software sensor, modelling and identification, control and optimization. In this paper a review of neural network application in the field of chemical engineering with emphasis on identification and process control is given. The neural networks have been proven usefull in the applications which include complex chemical and biochemical reactions. In such a processes use of standard methods of process modelling and control structure are frequently not suitable. The ability of neural network to model dynamics of nonlinear process makes them an important tool for implementation in model-based control. Due to intensively theory development and many practical applications, there are numerous neural network structures and algorithms. In this paper neural networks are categorized under three major control schemes: model-base predictive control, inverse model-based control, and adaptive control. The major applications are summarized. It reveals prospect of using neural networks in process identification and control. The future of neural network application lies not only in their explicite use, but in cross connecting to other advanted technnologies as well. Fusion of neural networks and fuzzy logic in the form of neural-fuzzy network is one of the possibilites. Other important field is hibrid modelling and identification methods which supplement simplified mechanistic models. Software sensors and their application, especially in controlling of bioprocesses, present a very promising field

The Equations of State of Real Gases

Prikazana su svojstva realnih plinova, a posebice njihova odstupanja od idealnosti. Dan je i kratak prikaz molekulskih međudjelovanja. Opisan je tradicijski model prikladan za jednoatomne plinove čije se molekule ponašaju kao klasične čestice. Prikazan je virijalni poučak, a od iskustvenih virijalnih jednadžbi stanja dan je Kamerlingh-Onnesov virijalni razvoj. Najpoznatija iskustvena jednadžba stanja - van der Waalsova - izvedena je s pomoću kanonske particijske funkcije. Predočene su i neke od važnijih jednadžbi stanja koje se rabe u tehničkoj termodinamici: Redlich-Kwongova, Peng-Robinsonova i Benedict-Webb-Rubinova jednadžba stanja. U pregledu statističkih modela izložen je rojni model, u kojemu se sva odstupanja od idealnosti opisuju konfiguracijskim integralom i navedena su njegova ograničenja, a prikazan je i Kirkwoodov model integralnih jednadžbi koji - za razliku od Mayerova rojnog modela - ne polazi od jednadžbe stanja idealnog plina, već gradi hijerarhiju raspodjelnih funkcija za mnoštvo molekula. Dan je i kratak pregled metodâ za numeričku simulaciju (Monte Carlo i molekulska dinamika).During the development of intelligent systems inspired by biological neural system, in the last two decades the researchers from various scientific fields have created neural networks for solving a series of problems from pattern recognition, prediction, diagnostic, software sensor, modelling and identification, control and optimization. In this paper a review of neural network application in the field of chemical engineering with emphasis on identification and process control is given. The neural networks have been proven usefull in the applications which include complex chemical and biochemical reactions. In such a processes use of standard methods of process modelling and control structure are frequently not suitable. The ability of neural network to model dynamics of nonlinear process makes them an important tool for implementation in model-based control. Due to intensively theory development and many practical applications, there are numerous neural network structures and algorithms. In this paper neural networks are categorized under three major control schemes: model-base predictive control, inverse model-based control, and adaptive control. The major applications are summarized. It reveals prospect of using neural networks in process identification and control. The future of neural network application lies not only in their explicite use, but in cross connecting to other advanted technnologies as well. Fusion of neural networks and fuzzy logic in the form of neural-fuzzy network is one of the possibilites. Other important field is hibrid modelling and identification methods which supplement simplified mechanistic models. Software sensors and their application, especially in controlling of bioprocesses, present a very promising field

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

An ARTMAP-incorporated Multi-Agent System for Building Intelligent Heat Management

This paper presents an ARTMAP-incorporated multi-agent system (MAS) for building heat management, which aims to maintain the desired space temperature defined by the building occupants (thermal comfort management) and improve energy efficiency by intelligently controlling the energy flow and usage in the building (building energy control). Existing MAS typically uses rule-based approaches to describe the behaviours and the processes of its agents, and the rules are fixed. The incorporation of artificial neural network (ANN) techniques to the agents can provide for the required online learning and adaptation capabilities. A three-layer MAS is proposed for building heat management and ARTMAP is incorporated into the agents so as to facilitate online learning and adaptation capabilities. Simulation results demonstrate that ARTMAP incorporated MAS provides better (automated) energy control and thermal comfort management for a building environment in comparison to its existing rule-based MAS approach

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented