@article {Antonelo2016, title = {Echo State Networks for Data-driven Downhole Pressure Estimation in Gas-lift Oil Wells}, journal = {Neural Networks}, volume = {85}, year = {2017}, note = {(in press)}, pages = {106-117}, abstract = {

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesir- able phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model sim- ulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neu- ral Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

}, doi = {10.1016/j.neunet.2016.09.009}, url = {http://authors.elsevier.com/sd/article/S0893608016301393}, author = {Eric A Antonelo and Eduardo Camponogara and Bjarne Foss} } @proceedings {Antonelo2015a, title = {An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well}, journal = {16th International Conference on Engineering Applications of Neural Networks}, volume = {Communications in Computer and Information Science, vol 517}, year = {2015}, pages = {379-389}, publisher = {Springer}, abstract = {

Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ei- ther has a high probability of failure or is unreliable due to harsh environ- ment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and tempera- ture in deepwater oil wells. In this paper, historical data obtained from an actual offshore oil well is used to build a black box model that estimates the PDG downhole pressure from platform variables, using Echo State Networks (ESNs), which are a class of recurrent networks with power- ful modeling capabilities. These networks, differently from other neural networks models used by most soft sensors in literature, can model the nonlinear dynamical properties present in the noisy real-world data by using a two-layer structure with efficient training: a recurrent nonlinear layer with fixed randomly generated weights and a linear adaptive read- out output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting.

}, isbn = {978-3-319-23981-1}, doi = {https://doi.org/10.1007/978-3-319-23983-5_35}, url = {https://link.springer.com/chapter/10.1007/978-3-319-23983-5_35}, author = {Eric A Antonelo and Eduardo Camponogara} } @article {Antonelo2008b, title = {Event detection and localization for small mobile robots using reservoir computing}, journal = {Neural Networks}, volume = {21}, number = {6}, year = {2008}, pages = {862{\textendash}871}, abstract = {Reservoir Computing (RC) techniques use a fixed (usually randomly created) re- current neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localiza- tion tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.}, doi = {doi:10.1016/j.neunet.2008.06.010}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {Antonelo2007, title = {Event detection and localization in mobile robot navigation using reservoir computing}, journal = {Artificial Neural Networks -- ICANN 2007}, volume = {Lecture Notes in Computer Science, vol 4669}, year = {2007}, pages = {660-669}, publisher = {Springer-Verlag}, edition = {de S{\'a} J.M., Alexandre L.A., Duch W., Mandic D. }, abstract = {

Reservoir Computing (RC) uses a randomly created recur- rent neural network where only a linear readout layer is trained. In this work, RC is used for detecting complex events in autonomous robot navi- gation. This can be extended to robot localization based solely on sensory information. The robot thus builds an implicit map of the environment without the use of odometry data. These techniques are demonstrated in simulation on several complex and even dynamic environments.

}, keywords = {ESN}, issn = {978-3-540-74695-9}, doi = {10.1007/978-3-540-74695-9_68}, url = {https://link.springer.com/chapter/10.1007/978-3-540-74695-9_68}, author = {Eric A Antonelo and Benjamin Schrauwen and Xavier Dutoit and Dirk Stroobandt and Marnix Nuttin} } @proceedings {Antonelo2007a, title = {Experiments with Reservoir Computing on the road sign problem}, journal = {Proceedings of the VIII Brazilian Congress on Neural Networks (CBRN)}, year = {2007}, publisher = {SBRN}, abstract = {

The road sign problem is tackled in this work with Reservoir Computing (RC) networks. These networks are made of a fixed recurrent neural network where only a readout layer is trained. In the road sign problem, an agent has to decide at some point in time which action to take given relevant information gathered in the past. We show that RC can handle simple and complex T-maze tasks (which are a subdomain of the road sign problem).

}, doi = {10.21528/CBRN2007-047}, url = {http://abricom.org.br/eventos/cbrn_2007/50100047-2/}, author = {Eric A Antonelo and Benjamin Schrauwen and Dirk Stroobandt} } @proceedings {292, title = {Evolutionary fuzzy system for architecture control in a constructive neural network}, journal = {6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {541-546}, publisher = {IEEE}, abstract = {

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi- Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554333}, url = {https://ieeexplore.ieee.org/abstract/document/1554333/}, author = {Rodrigo Calvo and Eric A Antonelo and Mauricio Figueiredo} }