Ignacio Santín

Bio: Ignacio Santín is an academic researcher from Autonomous University of Barcelona. The author has contributed to research in topics: Effluent & Model predictive control. The author has an hindex of 11, co-authored 38 publications receiving 440 citations.

ANN-Based Soft Sensor to Predict Effluent Violations in Wastewater Treatment Plants

Abstract: Wastewater treatment plants (WWTPs) form an industry whose main goal is to reduce water's pollutant products, which are harmful to the environment at high concentrations. In addition, regulations are applied by administrations to limit pollutant concentrations in effluent. In this context, control strategies have been adopted by WWTPs to avoid violating these limits; however, some violations still occur. For that reason, this work proposes the deployment of an artificial neural network (ANN)-based soft sensor in which a Long-Short Term Memory (LSTM) network is used to generate predictions of nitrogen-derived components, specifically ammonium ( S N H ) and total nitrogen ( S N t o t ). S N t o t is a limiting nutrient and can therefore cause eutrophication, while nitrogen in the S N H form is toxic to aquatic life. These parameters are used by control strategies to allow actions to be taken in advance and only when violations are predicted. Since predictions complement control strategies, the evaluation of the ANN-based soft sensor was carried out using the Benchmark Simulation Model N.2. (BSM2) and three different control strategies (from low to high control complexity). Results show that our proposed method is able to predict nitrogen-derived products with good accuracy: the probability of detecting violations of BSM2's limits is 86%⁻94%. Moreover, the prediction accuracy can be improved by calibrating the soft sensor; for example, perfect prediction of all future violations can be achieved at the expense of increasing the false positive rate.

Fuzzy Control and Model Predictive Control Configurations for Effluent Violations Removal in Wastewater Treatment Plants

Abstract: In this paper the following new control objectives for biological wastewater treatment plants (WWTPs) have been established: to eliminate violations of total nitrogen in the effluent (Ntot,e) or ammonium and ammonia nitrogen concentration (NH) in the effluent (NHe) and at the same time handle the customary requirements of improving effluent quality and reducing operational costs. The Benchmark Simulation Model No. 1 (BSM1) is used for evaluation, and the control is based on Model Predicitive Control (MPC) and fuzzy logic. To improve effluent quality and to reduce operational costs, a hierarchical control structure is implemented to regulate the dissolved oxygen (DO) on the three aerated tanks. The high level of this hierarchical structure is developed with a fuzzy controller that adapts the DO set points of the low level based on the NH concentration in the fifth tank (NH5). The low level is composed of three MPC controllers with feedforward control (MPC + FF). For avoiding violations of Ntot,e, a second ...

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Knowledge-Based Systems

Abstract: Knowledge Based Systems (KBS) are systems that use artificial intelligence techniques in the problem solving process. This text is designed to develop an appreciation of KBS and their architecture and to help users understand a broad variety of knowledge based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters are designed to be modular providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material being presented and to stimulate thought and discussion.

Handbook of game theory with economic applications

Abstract: Preface (R.J. Aumann, S. Hart). Strategic equilibrium (E. van Damme). Foundations of strategic equilibrium (J. Hillas, E. Kohlberg). Incomplete information (R.J. Aumann, A. Heifetz). Non-zero-sum two-person games (T.E.S. Raghavan). Computing equilibria for two-person games (B. von Stengel). Non-cooperative games with many players (M. Ali Khan, Y. Sun). Stochastic games (J-F. Mertens). Stochastic games: recent results (N. Vieille). Game theory and industrial organization (K. Bagwell, A. Wolinsky). Bargaining with incomplete information (L.M. Ausubel, P. Cramton, R.J. Deneckere). Inspection Games (R. Avenhaus, B.V. Stengel, S.Zamir). Economic history and game theory (A. Greif). The shapley value (E. Winter). Variations on the shapley value (D. Monderer, D. Samet). Values of non-transferable utility games (R. McLean). Values of games with infinitely many players (A. Neyman). Values of perfectly competitive economies (S. Hart). Some other economic applications of the value (J-F. Mertens). Strategic aspects of political systems (J. Banks). Game-theoretic analysis of legal rules and institutions (J-P. Benoit, L.A. Kornhauser). Implementation Theory (T. Palfrey). Game Theory and experimental Gaming (M. Shubik).

A Survey on Deep Learning for Data-Driven Soft Sensors

Abstract: Soft sensors are widely constructed in process industry to realize process monitoring, quality prediction, and many other important applications. With the development of hardware and software, industrial processes have embraced new characteristics, which lead to the poor performance of traditional soft sensor modeling methods. Deep learning, as a kind of data-driven approach, shows its great potential in many fields, as well as in soft sensing scenarios. After a period of development, especially in the last five years, many new issues have emerged that need to be investigated. Therefore, in this article, the necessity and significance of deep learning for soft sensor applications are demonstrated first by analyzing the merits of deep learning and the trends of industrial processes. Next, mainstream deep learning models, tricks, and frameworks/toolkits are summarized and discussed to help designers propel the developing progress of soft sensors. Then, existing works are reviewed and analyzed to discuss the demands and problems occurred in practical applications. Finally, outlook and conclusions are given.