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I Rodríguez-Roda

Publications and source records attributed to I Rodríguez-Roda.

8 recordsLinked to original sources

Improving the efficiency of case-based reasoning to deal with activated sludge solids separation problems.

The potential of Case-Based Reasoning to use the knowledge gained from past experiences to solve problematic situations has made this Artificial Intelligence technique a useful decision support tool in different environmental domains such as wastewater treatment. Case-Based Reasoning tools automatically identify similarities between present and previous situations (cases) and reuse the experiences gained from the previous situations to solve current problems. Case retrieval can be considered to be the most important step in the process of Case-Based Reasoning. In the present study we propose incorporating a relevance network in order to increase the accuracy and the efficiency of case retrieval. The result is a context-sensitive feature-weighting methodology capable of defining the model of relationships between the different attributes or features that define the context in which Case-Based Reasoning is applied. These features affect the retrieval procedure directly. The feature's degree of relevance in the network is easily translated into a set of simple rules and applied during case retrieval, specifically during the similarity calculation. The results obtained in the present study show significant improvements in the accuracy of case retrieval. With the approach presented here experts considered more than 90% of the retrieved cases to be completely relevant according to the knowledge these cases provided for dealing with solids separation problems.

Artificial Intelligence↗

Dynamic reasoning to solve complex problems in activated sludge processes: a step further in decision support systems.

Decision support systems (DSS) have generated high expectations as a tool to support activated sludge operation because of their ability to represent heuristic reasoning and to handle large amounts of qualitative, uncertain and low-accuracy data. Previous applications have been satisfactory to control simple problems, when static reasoning and literature-based solutions were enough. However to face complex operational problems with biological origin and slow dynamics (e.g. solids separation problems), it is necessary to use dynamic reasoning and apply long-term control strategies, monitoring the evolution of the process and adjusting the action plan according to the feed back of the process. This paper presents a dynamic reasoning DSS to face solids separation problems in the activated sludge system. The DSS is capable of identifying the complex problem affecting the process, determining if the current situation is new or a continuation from the previous one, assessing what is the specific cause of the situation, and recommending a long-term control strategy, which is daily adjusted according to the evolution of the process.

Artificial Intelligence↗

Case-based reasoning, a promising tool to face solids separation problems in the activated sludge process.

Classical control has serious limitations when faced with solids separation problems in the activated sludge process. Lack of knowledge about the mechanisms involved in the imbalance within the different microbiological communities implies that a general solution to these undesirable situations has not yet been provided. However, operators have to make decisions based on their experience and intuition to solve the problem (or at least to minimise the effects). The acquisition and registration of the knowledge learnt from each new experience can be decisive when solving similar problems in the future. Case-based reasoning (CBR) is an advanced technique for knowledge management in complex systems that uses past experiences to solve brand new situations. Previous simplified proposals in this field have exposed limitations, but this paper describes a new approach to CBR, considering the dynamics and the complexity of solids separation problems.

Decision Support Techniques↗

Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process.

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.

Artificial Intelligence↗

Extension of the IWA/COST simulation benchmark to include expert reasoning for system performance evaluation.

In this paper the development of an extension module to the IWA/COST simulation benchmark to include expert reasoning is presented. This module enables the detection of suitable conditions for the development of settling problems of biological origin (filamentous bulking, foaming and rising sludge) when applying activated sludge control strategies to the simulation benchmark. Firstly, a flow diagram is proposed for each settling problem, and secondly, the outcome of its application is shown. Results of the benchmark for two evaluated control strategies illustrate that, once applied to the simulation outputs, this module provides supplementary criteria for plant performance assessment. Therefore, simulated control strategies can be evaluated in a more realistic framework, and results can be recognised as more realistic and satisfactory from the point of view of operators and real facilities.

Benchmarking↗

Environmental engineering education in Spain.

There is a growing demand for engineers and technologists who show multidisciplinary expertise to deal with environmental issues. As a result of this demand, most countries are adapting their old university programs on environmental engineering education. In Spain an official environmental engineering degree does not yet exist, but the Council of Universities is working to present a proposal, based on Bologna agreement concepts. The paper summarizes not only the future perspectives of environmental engineering education in Spain, but also the evolution of the approach during the last decades, which includes the role of the private initiative, the environmental sciences degree, and the intensification in different traditional engineering degrees. Finally, the paper briefly details and compares the syllabus developed in the only four Spanish universities where environmental engineering is offered as a non-official post-graduate course lasting two years.

Curriculum↗

A knowledge-based approach to the deflocculation problem: integrating on-line, off-line, and heuristic information.

A knowledge-based approach for the supervision of the deflocculation problem in activated sludge processes was considered and successfully applied to a full-scale plant. To do that, a methodology that integrates on-line, off-line and heuristic information has been proposed. This methodology consists of three steps: (i). development of a decision tree (which involves knowledge acquisition and representation); (ii). implementation into a rule-based system; and (iii). validation. The set of symptoms most useful in diagnosing the deflocculation problem has been identified, the different branches to diagnose pin-point floc and dispersed growth have been built (using generic and specific knowledge), and all this knowledge has been codified into an object-oriented shell. The results obtained in the application of this knowledge-based approach to the Granollers WWTP (which treats about 130000 inhabitants-equivalents) showed that the system was able to identify correctly the problem with reasonable accuracy. Our positive experience building this system suggests that this approach is a practical and valuable element to include in an intelligent supervisory system combining numerical and reasoning techniques.

Artificial Intelligence↗

Enhancing biological nitrogen removal in a small wastewater treatment plant by regulating the air supply.

Most of the small wastewater treatment plants in Catalonia were not designed to remove nutrients. However, the large safety margins built into their original engineering design has meant that, in many cases, it has been possible to remove part of the nitrogen without being controlled. This paper focuses on the practical experience carried out in one of these facilities, the Bisbal wastewater treatment plant, aimed at enhancing the biological nitrogen removal using simple, low-cost, easy-to-operate measurement and control. The control strategy, which is based on air supply regulation, was previously designed using simulation studies. A dissolved oxygen monitoring system was installed into the facility, while nitrogen concentrations were analysed every day. Optimal dissolved oxygen profiles in the oxidation ditch were identified, while the air supply strategy was modified according to the variations in the influent.

Air↗