Manel Poch

Designing and building real environmental decision support systems

The complexity of environmental problems makes necessary the development and application of new tools capable of processing not only numerical aspects, but also experience from experts and wide public participation, which are all needed in decision-making processes. Environmental decision support systems (EDSSs) are among the most promising approaches to confront this complexity. The fact that different tools (artificial intelligence techniques, statistical/numerical methods, geographical information systems, and environmental ontologies) can be integrated under different architectures confers EDSSs the ability to confront complex problems, and the capability to support learning and decision-making processes. In this paper, we present our experience, obtained over the last 10 years, in designing and building two real EDSSs, one for wastewater plant supervision, and one for the selection of wastewater treatment systems for communities with less than 2000 inhabitants. The flow diagram followed to build the EDSS is presented for each of the systems, together with a discussion of the tasks involved in each step (problem analysis, data collection and knowledge acquisition, model selection, model implementation, and EDSS validation). In addition, the architecture used is presented, showing how the five levels on which it is based (data gathering, diagnosis, decision support, plans, and actions) have been implemented. Finally, we present our opinion on the research issues that need to be addressed in order to improve the ability of EDSSs to cope with complexity in environmental problems (integration of data and knowledge, improvement of knowledge acquisition methods, new protocols to share and reuse knowledge, development of benchmarks, involvement of end-users), thus increasing our understanding of the environment and contributing to the sustainable development of society.  2003 Elsevier Ltd. All rights reserved.

Artificial Intelligence and Environmental Decision Support Systems

An effective protection of our environment is largely dependent on the quality of the available information used to make an appropriate decision. Problems arise when the quantities of available information are huge and nonuniform (i.e., coming from many different disciplines or sources) and their quality could not be stated in advance. Another associated issue is the dynamical nature of the problem. Computers are central in contemporary environmental protection in tasks such as monitoring, data analysis, communication, information storage and retrieval, so it has been natural to try to integrate and enhance all these tasks with Artificial Intelligence knowledge-based techniques. This paper presents an overview of the impact of Artificial Intelligence techniques on the definition and development of Environmental Decision Support Systems (EDSS) during the last fifteen years. The review highlights the desirable features that an EDSS must show. The paper concludes with a selection of successful applications to a wide range of environmental problems.

Reaction scheme of lipase production byCandida rugosa growing on olive oil

SummaryIn a study of lipase production byCandida rugosa growing on olive oil, the relationship between the consumption of substrate and lipase production is presented. Two stages could be observed in the consumption of the olive oil: a first one, related with the glycerol depletion without lipase production, and a second one, associated with the fatty acids consumption when the enzyme appears in the medium.

Prediction of the bulking phenomenon in wastewater treatment plants

The control and prediction of wastewater treatment plants poses an important goal: to avoid breaking the environmental balance by always keeping the system in stable operating conditions. It is known that qualitative information — coming from microscopic examinations and subjective remarks — has a deep influence on the activated sludge process. In particular, on the total amount of effluent suspended solids, one of the measures of overall plant performance. The search for an input‐output model of this variable and the prediction of sudden increases (bulking episodes) is thus a central concern to ensure the fulfillment of current discharge limitations. Unfortunately, the strong interrelation between variables, their heterogeneity and the very high amount of missing information makes the use of traditional techniques difficult, or even impossible. Through the combined use of several methods — rough set theory and artificial neural networks, mainly — reasonable prediction models are found, which also serve to show the different importance of variables and provide insight into the process dynamics. q 2000 Elsevier Science Ltd. All rights reserved.

Risk assessment modelling of microbiology-related solids separation problems in activated sludge systems

This paper proposes a risk assessment model for settling problems of microbiological origin in activated sludge systems (filamentous bulking, foaming and rising sludge). The aim of the model is not to diagnose microbiology-related solids separation problems with absolute certainty but to quantify in dynamic scenarios whether simulated operational procedures and control strategies lead to favourable conditions for them to arise or not. The rationale behind the model (which integrates the mechanisms of standard activated sludge models with empirical knowledge), its implementation in a fuzzy rule-based system and the details of its operation are illustrated in the different sections of the paper. The performance of the risk assessment model is illustrated by evaluating a number of control strategies facing different short-term influent conditions as well as long-term variability using the IWA/COST simulation benchmark. The results demonstrate that some control strategies, although performing better regarding operating costs and effluent quality, induce a higher risk for solids separation problems. In view of these results, it is suggested to integrate empirical knowledge into mechanistic models to increase reliability and to allow assessment of potential side-effects when simulating complex processes.

DAI-DEPUR: an integrated and distributed architecture for wastewater treatment plants supervision

The activated sludge process — the main biological technology usually applied to wastewater treatment plants (WWTP) — directly depends on live beings (microorganisms), and therefore on unforeseen changes produced by them. It could be possible to get a good plant operation if the supervisory control system is able to react to the changes and deviations in the system and can take the necessary actions to restore the systems performance. These decisions are often based both on physical, chemical, microbiological principles (suitable to be modelled by conventional control algorithms) and on some knowledge (suitable to be modelled by knowledge-based systems). But one of the key problems in knowledge-based control systems design is the development of an architecture able to manage efficiently the different elements of the process (integrated architecture), to learn from previous cases (specific experimental knowledge) and to acquire the domain knowledge (general expert knowledge). These problems increase when the process belongs to an ill-structured domain and is composed of several complex operational units. Therefore, an integrated and distributed AI architecture seems to be a good choice. This paper proposes an integrated and distributed supervisory multi-level architecture for the supervision of WWTP, that overcomes some of the main troubles of classical control techniques and those of knowledge-based systems applied to real world systems.

Towards a model of input–output behaviour of wastewater treatment plants using soft computing techniques

Abstract Wastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keeping the system in stable operating conditions and suitable decision-making. In this respect, the availability of models characterizing WWTP behaviour as a dynamic system is a necessary first step. However, due to the high complexity of the WWTP processes and the heterogeneity, incompleteness and imprecision of WWTP data, and finding suitable models poses substantial problems. In this work, an approach via soft computing techniques is sought, in particular, by experimenting with fuzzy heterogeneous time-delay neural networks to characterize the time variation of outgoing variables. Experimental results show that these networks are able to characterize WWTP behaviour in a statistically satisfactory sense and also that they perform better than other well-established neural network models.

Occurrence and in-stream attenuation of wastewater-derived pharmaceuticals in Iberian rivers

A multitude of pharmaceuticals enter surface waters via discharges of wastewater treatment plants (WWTPs), and many raise environmental and health concerns. Chemical fate models predict their concentrations using estimates of mass loading, dilution and in-stream attenuation. However, current comprehension of the attenuation rates remains a limiting factor for predictive models. We assessed in-stream attenuation of 75 pharmaceuticals in 4 river segments, aiming to characterize in-stream attenuation variability among different pharmaceutical compounds, as well as among river segments differing in environmental conditions. Our study revealed that in-stream attenuation was highly variable among pharmaceuticals and river segments and that none of the considered pharmaceutical physicochemical and molecular properties proved to be relevant in determining the mean attenuation rates. Instead, the octanol-water partition coefficient (Kow) influenced the variability of rates among river segments, likely due to its effect on sorption to sediments and suspended particles, and therefore influencing the balance between the different attenuation mechanisms (biotransformation, photolysis, sorption, and volatilization). The magnitude of the measured attenuation rates urges scientists to consider them as important as dilution when aiming to predict concentrations in freshwater ecosystems.

A comparative study on the use of similarity measures in case-based reasoning to improve the classification of environmental system situations

The step of identifying to which class of operational situation belongs the current environmental system (ES) situation is a key element to build successful environmental decision support systems (EDSS). This diagnosis phase is especially difficult due to multiple features involved in most environmental systems. It is not an easy task for environmental managers to acquire, to integrate and to understand all the increasing amount of data obtained from an environmental process and to get meaningful knowledge from it. Thus, a deeper classification task in a EDSS needs a full integration of gathered data, including the use of statistics, pattern recognition, clustering techniques, similarity-based reasoning and other advanced information technology techniques. Consequently, it is necessary to use automatic knowledge acquisition and management methods to build consistent and robust decision support systems. Additionally, some environmental problems can only be solved by experts who use their own experience in the resolution of similar situations. This is the reason why many artificial intelligence (AI) techniques have been used in recent past years trying to solve these classification tasks. Integration of AI techniques in EDSS has led to more accurate and reliable EDSS. Case-based reasoning (CBR) is a good technique to solve new problems based on previous experience. Main assumption in CBR relies on the hypothesis that similar problems should have similar solutions. When working with labelled cases, the retrieval step in CBR cycle can be seen as a classification task. The new cases will be labelled (classified) with the label (class) of the most similar case retrieved from the case base. In environmental systems, these classes are operational situations. Thus, similarity measures are key elements in obtaining a reliable classification of new situations. This paper describes a comparative analysis of several commonly used similarity measures, and a study on its performance for classification tasks. In addition, it introduces L’Eixample distance, a new similarity measure for case retrieval. This measure has been tested with good accuracy results, which improve the performance of the classification task. The testing has been done using two environmental data sets and other data sets from the UCI Machine Learning Database Repository.  2003 Elsevier Ltd. All rights reserved.

Fermentation Behaviour of Lipase Production by Candida rugosa Growing on Different Mixtures of Glucose and Olive Oil

Abstract In order to suggest a production process that reduces fermentation time and supplies the proper characteristics to the culture broth for a better recovery of lipase, the influence of three parameters over lipase production by Candida rugosa has been studied. It has been found that oxygen deficiency restricts lipase production by this microorganism, while a shortage of nitrogen source seems to have no effect on lipase production when glucose and olive oil are used jointly as carbon sources. Among the substrates tested, the best results are obtained with the use of olive oil as carbon source. Not only does this procedure provide the highest lipase production found in this work, but this production is also related with the growth.