The smelting and converting El Teniente process for copper concentrates as well as slag cleaning furnace technologies to treat slags coming out from the El Teniente furnaces are widely applied today in Chile and around the world. Several industrial applications of this process have shown their ability to treat copper concentrates in a wide range of chemical and mineralogical compositions. Main operational parameters determining performance of the process are: oxygen enriched air flow rate; degree of oxygen enrichment; moisture content of the solid materials processed; molten material levels inside the vessel; frequency of molten materials tapping; bath temperature; and copper losses in slags. In this work, the copper losses in the slags from the El Teniente pyrometallurgical process are predicted by calculation from thermodynamic data and compared with those determined by microscopic examination and slag industrial data. Results obtained show that the main part of copper losses in the slag from the El Teniente furnace are mechanically entrapped or floated unsettled droplets of the co-existing matte phase. Conversely, the very low copper losses in the slag from the slag cleaning furnace are limited by the physicochemical form.I. Imris is in the

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 system's 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 (spec@c 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.

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