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 interrelationbetween 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

Application of a support system to the design of wastewater treatment plants

This paper presents a case study that explores the advantages that can be derived from the use of a design support system during the design of wastewater treatment plants (WWTP). With this objective in mind a simplified but plausible WWTP design case study has been generated with KBDS, a computer-based support system that maintains a historical record of the design process. The study shows how, by employing such a historical record, it is possible to: (1) rank different design proposals responding to a design problem; (2) study the influence of changing the weight of the arguments used in the selection of the most adequate proposal; (3) take advantage of keywords to assist the designer in the search of specific items within the historical records; (4) evaluate automatically thecompliance of alternative design proposals with respect to the design objectives; (5) verify the validity of previous decisions after the modification of the current constraints or specifications; (6) re-use the design records when upgrading an existing WWTP or when designing similar facilities; (7) generate documentation of the decision making process; and (8) associate a variety of documents as annotations to any component in the design history. The paper also shows one possible future role of design support systems as they outgrow their current reactive role as repositories of historical information and start to proactively support the generation of new knowledge during the design process

Production of polyesters in transgenic plants.

Polyhydroxyalkanoates (PHAs) are bacterial polyesters having the properties of biodegradable thermoplastics and elastomers. Synthesis of PHAs has been demonstrated in transgenic plants. Both polyhydroxybutyrate and the co-polymer poly(hydroxybutyrate-co-hydroxyvalerate) have been synthesized in the plastids of Arabidopsis thaliana and Brassica napus. Furthermore, a range of medium-chain-length PHAs has also been produced in plant peroxisomes. Development of agricultural crops to produce PHA on a large scale and at low cost will be a challenging task requiring a coordinated and stable expression of several genes. Novel extraction methods designed to maximize the use of harvested plants for PHA, oil, carbohydrate, and feed production will be needed. In addition to their use as plastics, PHAs can also be used to modify fiber properties in plants such as cotton. Furthermore, PHA can be exploited as a novel tool to study the carbon flux through various metabolic pathways, such as the fatty acid beta-oxidation cycle.