Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid

This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. Different structures of NARMA (Non-linear ARMA) models have been studied. The experimental results have allowed to carry out a comparison between the different neural approaches and a first-principles model. The best neural results are obtained using a parallel model structure based on a recurrent neural network architecture, which guarantees better dynamic approximations than currently employed neural models. The results suggest that parallel models built up with recurrent networks can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits which change from batch installation to installation.Publicad

Applications of recurrent neural networks in batch reactors. Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

Although nonlinear inverse and predictive control techniques based on artificial neural networks have been extensively applied to nonlinear systems, their use in real time applications is generally limited. In this paper neural inverse and predictive control systems have been applied to the real-time control of the heat transfer fluid temperature in a pilot chemical reactor. The training of the inverse control system is carried out using both generalised and specialised learning. This allows the preparation of weights of the controller acting in real-time and appropriate performances of inverse neural controller can be achieved. The predictive control system makes use of a neural network to calculate the control action. Thus, the problems related to the high computational effort involved in nonlinear model-predictive control systems are reduced. The performance of the neural controllers is compared against the self-tuning PID controller currently installed in the plant. The results show that neural-based controllers improve the performance of the real plant.Publicad

PNNARMA model: an alternative to phenomenological models in chemical reactors

This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits.Publicad

Forecasting high waters at Venice Lagoon using chaotic time series analisys and nonlinear neural netwoks

Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980-1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2-3 hours ahead.Publicad

Lazy training of radial basis neural networks

Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space. This makes non-local methods, like Neural Networks, not very accurate in those cases. On the other hand, local methods have the problem of how to know which are the best examples for each test pattern. In this work, we present a way of performing a trade off between local and non-local methods. On one hand a Radial Basis Neural Network is used like learning algorithm, on the other hand a selection of the training patterns is used for each query. Moreover, the RBNN initialization algorithm has been modified in a deterministic way to eliminate any initial condition influence. Finally, the new method has been validated in two time series domains, an artificial and a real world one.This article has been financed by the Spanish founded research MEC project OPLINK::UC3M, Ref: TIN2005-08818-C04-0

The use of neural networks for fitting complex kinetic data

Congrès ESCAPE-3: European Symposium on Computer Aided Process Engineering n.3, Graz , Autriche, 1993In this paper the use of neural networks for fitting complex kinetic data is discussed. To assess the validity of the approach two different neural network architectures are compared with the traditional kinetic identification methods for two cases: the homogeneous esterification reaction between propionic anhydride and 2-butanol. catalysed by sulphuric acid and the heterogeneous liquid-liquid toluene mononitration by mixed acid. A large set of experimental data obtained by adiabatic and heat flux calorimetry and by gas chromatography is used for the training of the neural networks. The results indicate that the neural network approach can be used to deal with the fitting of complex kinetic data to obtain an approximate reaction rate function in a limited amount of time which can be used for design improvement or optimisation when owing to small production levels or time constraints. it is not possible to develop a detailed kinetic analysis.Publicad

Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-

Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks

Time series analysis using nonlinear dynamics systems theory and multilayer neural networks models have been applied to the time sequence of water level data recorded every hour at 'Punta della Salute' from Venice Lagoon during the years 1980–1994. The first method is based on the reconstruction of the state space attractor using time delay embedding vectors and on the characterisation of invariant properties which define its dynamics. The results suggest the existence of a low dimensional chaotic attractor with a Lyapunov dimension, DL, of around 6.6 and a predictability between 8 and 13 hours ahead. Furthermore, once the attractor has been reconstructed it is possible to make predictions by mapping local-neighbourhood to local-neighbourhood in the reconstructed phase space. To compare the prediction results with another nonlinear method, two nonlinear autoregressive models (NAR) based on multilayer feedforward neural networks have been developed. From the study, it can be observed that nonlinear forecasting produces adequate results for the 'normal' dynamic behaviour of the water level of Venice Lagoon, outperforming linear algorithms, however, both methods fail to forecast the 'high water' phenomenon more than 2–3 hours ahead

Modeling approach to regime shifts of primary production in shallow coastal ecosystems

Pristine coastal shallow systems are usually dominated by extensive meadows of seagrass species, which are assumed to take advantage of nutrient supply from sediment. An increasing nutrient input is thought to favour phytoplankton, epiphytic microalgae, as well as opportunistic ephemeral macroalgae that coexist with seagrasses. The primary cause of shifts and succession in the macrophyte community is the increase of nutrient load to water; however temperature plays also an important role. A competition model between rooted seagrass (Zostera marina), macroalgae (Ulva sp), and phytoplankton has been developed to analyse the succession of primary producer communities in these systems. Successions of dominance states, with different resilience characteristics, are found when modifying the input of nutrients and the seasonal temperature and light intensity forcing.Comment: 33 pages, including 10 figures. To appear in Ecological Complexit

Las fallas y pliegues recientes y activos de la parte centro-oriental de las Zonas Internas de la Cordillera Bética

The most recent tectonic structures of the central-eastern Internal Zones of the Betic Cordillera (from 3.1ºW to 1.7ºW and to the south of 37.525ºN) include fault and folds developed from the Late Miocene onwards, which are related to N-S/NW-SE directed continental collision and moderate thickening of a crust that is relatively hot at depth. In this setting, E-W to WSW-ENE folds, with locally associated E-W transpressive right-lateral and reverse faults, favoured the emersion of the northern Alborán basin palaeomargin and the progressive intramontane basin disconnection. The NNE-SSW to NE-SW trending regional left-lateral Palomares and Carboneras fault zones are dominant structures in the easternmost part of the cordillera. In addition, NW-SE to WNWESE trending normal and oblique-slip normal faults are widespread. The collision is still active and continues to drive active folds and faults, some probably being the likely source of moderate-sized earthquakes. The Campo de Dalías and surrounding sectors, deformed by active ENE-WSW folds and NW-SE to WNW-ESE oblique-slip normal faults, are probably the sites with the largest concentration of significant earthquakes during recent years. Moderate-magnitude earthquakes (Mw 5.0 to 6.5) have occurred there at fairly regular intervals, in 1804, 1910, and 1994. Toward the east, NW-SE trending normal faults extending from Almería to the Tabernas basin deform the Quaternary rocks with associated moderate seismicity (the 2002 Gergal Mw 4.7 earthquake, and possibly the 1894 Nacimiento earthquake, felt with intensity VII). In the Sorbas-Vera basin, the Palomares fault zone is also responsible for moderate-sized earthquakes (1518 Vera earthquake). In the Almanzora corridor, NW-SE to WNW-ESE trending Lúcar-Somontín faults also could be considered one of the possible source of moderate-magnitude seismicity (1932 Lúcar, Mw 4.8 earthquake felt with intensity VIII). Toward the east, between Albox and Partaloa, several small reverse faults and associated compressive structures deform Quaternary alluvial and fluvial sediments. Although some of these folds reveal a slow and progressive deformation from the Middle Pleistocene onwards, some of these reverse fault segments that deform the western Huércal-Overa basin could host the 1972 NW Partaloa, mbLg 4.8 earthquake, felt with intensity VII.Las estructuras tectónicas más recientes que deforman la parte centro-oriental de las Zonas Internas de Cordillera Bética (entre 3.1º y 1.7ºO y al sur de 37.525ºN) son fallas y pliegues que comenzaron a formarse aproximadamente en el Mioceno superior en un contexto de colisión continental N-S/NO-SE y moderado engrosamiento cortical. En este marco tectónico, pliegues y fallas transpresivas dextras e inversas de direcciones E-O/OSO-ENE favorecieron la emersión del borde norte de la paleocuenca de Alborán y la progresiva desconexión de pequeñas cuencas intramontañosas. Además, comenzaron a formarse las grandes zonas de falla de Palomares y Carboneras, con direcciones NNE-SSO y NE-SO respectivamente y movimientos sinistros, que también han condicionado la evolución de la Cordillera Bética oriental desde el Mioceno superior. Algunas fallas con salto normal/normal-oblicuo y trazas NO-SE/ONO-ESE también se han desarrollado ampliamente en toda la zona de estudio. La colisión, aún activa, permite que algunos pliegues y fallas continúen propagándose en la actualidad, eventualmente causando terremotos con magnitudes moderadas. El Campo de Dalías y los sectores adyacentes, deformados por pliegues activos de direcciones ENE-OSO y fallas NO-SE/ONOESE normales-oblicuas, probablemente representan la zona con mayor concentración de terremotos importantes (Mw 5.0-6.5) con eventos recurrentes en 1804, 1910 y 1994. Al este del Campo de Dalías, una amplia zona de falla normal se extiende en dirección NO-SE desde Almería hasta la cuenca de Tabernas. Esta zona de falla muestra evidencias de funcionamiento durante el Cuaternario y tiene sismicidad moderada asociada a su terminación septentrional (el terremoto de Gergal en 2002 con Mw 4.7; y posiblemente el terremoto de Nacimiento en 1894 con intensidad VII). La zona de falla de Palomares es también responsable de terremotos moderados en la Cuenca de Sorbas-Vera (terremoto de Vera en 1518). En la parte central del corredor del Almanzora, alguno de los segmentos de falla normal que se extienden entre Lúcar y Somontín podría ser responsable del terremoto de Lúcar, en 1932 (Mw 4.8 e intensidad VIII). Al este, entre Albox y Partaloa, se han descrito fallas inversas y pliegues asociados que deforman sedimentos cuaternarios. Aunque algunas de estas estructuras muestran evidencias de funcionamiento lento y progresivo durante el Cuaternario, el terremoto de Partaloa en 1972 (mbLg 4.8 e intensidad VII) pudo ser causado por la actividad de cualquiera de estos segmentos de falla inversa que deforman la parte occidental de la cuenca de Huércal-Overa