Ingeniería y Tecnología

"La Universidad no se hace responsable por los conceptos emitidos por sus alumnos en sus trabajos de tesis. Solo velará por que no se publique nada contrario al dogma y a la moral católica y por que las tesis no contengan ataques personales contra persona alguna, antes bien se vea en ellas el anhelo de buscar la verdad y la justicia".

Respuestas del Taller: Tarea 1. Caso de estudio. Para la siguiente situación, el grupo de trabajo deberá realizar una lista de algunos objetos detectados en el caso de estudio que se describe más adelante, para luego clasificarlos y determinar sus atributos. Seguidamente, deberán establecer las asociaciones de los objetos entre sí, estimando ciertos atributos si es que aplican a este contexto. Importante: Pueden revisar el ejemplo resuelto en la presentación de la clase número 3 como ayuda para resolver esta tarea.

H ernan V illanueva ² and H armeet L amba W e present details of the optimization task in a real-time (Thom pson and Mertz 1993) knowledge-based (Moore et al. 1991, L arsson 1992) supervision support system in the coal washing domain. The Ash Control Model (AshMod) assists operators in maximizing clean coal yield while keeping ash (impurity) content within acceptable limits. AshMod assists the operator in plant situation assessment, fault diagnosis, and performance optimization. Situation assessment and fault diagnosis are mentioned brie¯y, since they have been described elsewhere in detail (V illanueva and L amba 1997, 1998). W e focus on the optimization task, which employs a hybrid arti® cial intelligence and operations research approach. The process is modelled through a set of extended states associated with the entire process and with individual components (circuits) within the plant. The scheduling optimizer continuously monitors the process, assesses the process state, and dynam ically plans and performs integer and real optimization of a sequence of actions. The supervision support system captures domain knowledge through multiview models (Terspra et al. 1993) such as the goal tree success tree, plant schematic and fault cause network. AshMod performs deep reasoning through the use of knowledge models (L ind 1994) that capture purpose, function, structure, behaviour and heuristics. These knowledge models are mentioned brie¯y, since they have also been described elsewhere in detail (

We present a real-time knowledge based [8] supervision support system in the coal washing domain. The Ash Control Model (AshMod) assists operators in maximising clean coat yield while keeping ash (impurity) content within acceptable limits. AshMod assists the operator in plant situation assessment, fault diagnosis, and performance optimisation. In this paper, we describe the optimisation task, which employs a hybrid artificial intelligence and operations research approach. The process is modeled through a set of extended states associated with the entire process and with individual components (circuits) within the plant. The process is continuously monitored to assess the process state, which dynamically influences the planning and scheduling of a sequence of optimisation steps. The supervision support system captures domain knowledge through multiview knowledge models [7] that capture purpose, function, structure, behaviour and heuristics. The supervision support system is currently undergoing online validation at the B&C Coal Washing Plants operated by the Broken Hill Proprietary Limited (BHP) at Port Kembla, Australia [1 ].

This paper provides detail of the temporal extensio n considerations of persistence and aggregation as th ey apply to the storage of patient history. This is described within the context of a KBS architecture for medical applications, specifically a fetal health d ecision support system module to provide diagnosis of pregnancy-induced hypertension, or pre-eclampsia.

In this paper we present monitoring, diagnosis and optimization in the Ash Control Model (AshMod), a knowledge-based operator guidance system (OGS) in the coal washing domain. AshMod assists the operator in monitoring the plant, performing fault diagnosis, and in plant optimization. It assists the operators in maximizing clean coal yield while keeping ash (impurity) content within acceptable limits. AshMod performs deep reasoning through the use of knowledge models that capture purpose, function, structure, behaviour and heuristics. Knowledge validation and maintenance is facilitated through the use of graphical object-oriented knowledge models.

We present the analysis, design and implementation of a knowledge based operator guidance system (OGS) in the coal washing domain. AshMod integrates the KADS methodology for the development of knowledge based systems with the G2 real time implementation environment. It assists the operator in monitoring the plant, performing fault diagnosis, and in plant optimisation. With the decision support provided by AshMod, the operator is able to control the amount of ash (impurity) in clean coal, hence increasing plant productivity. One of our goals in developing AshMod was to implement a high quality knowledge based system that would assist validation and maintenance. This can be very difficult in a traditional rule based systems, but is made substantially easier through the graphical model based representations of domain knowledge. AshMod can perform deep reasoning through the use of knowledge models that capture purpose, function, structure, behaviour and heuristics. The KADS methodology provides a systematic approach to the development of such knowledge models. Knowledge validation and maintenance is facilitated through the development of graphical object oriented knowledge models in G2. AshMod is currently undergoing offline testing at the coal washing plants operated by BHP at Port Kembla, Australia.

Representa de forma gráfica y simbólica, el comportamiento de las funciones. Indicaciones: • Aplique la transformación de funciones en los ejercicios que lo ameriten. • Dibuje con lápiz y papel los gráficos de las de las funciones solicitadas. • La solución de esta guía estará disponible para que pueda contrastar sus respuestas, a partir de la clase siguiente. PROBLEMAS 1. Representar en forma gráfica las funciones siguientes: a) í µí±“(í µí±¥) = 1 2 í µí±¥ − 1; b) í µí±”(í µí±¥) = −2í µí±¥ − 1; c) ℎ(í µí±¥) = í µí±¥ 2 + 3 d) í µí±—(í µí±¥) = í µí±¥ 3 − 2 e) í µí±˜(í µí±¥) = −í µí±¥ 2 − 1 f) í µí±™(í µí±¥) = −í µí±¥ 3 + 2

i Resumen El presente trabajo de Tesis de Magíster en Minería, tiene como objetivo estudiar los distintos factores que interactúan en términos de competitividad y atractividad en el mercado mundial del cobre y analizar los desafíos y estrategias de las cinco principales empresas productoras en la actualidad: Codelco, Freeport McMoran, BHP Billiton, Xstrata Copper y Rio Tinto. El periodo considerado en el análisis corresponde al quinquenio 2007-2011 y desde el punto de vista de la competitividad se escogieron las cuatro o cinco faenas de mayor producción para cada compañía.

D = 8000 Graficar la curva de demanda (X D 1000, P D 8000) Reemplazar en la ecuación de oferta X o =0 P O = 1000 P O =1000+2 X O P O =1000+2·0 P O =1000 Graficar la curva de oferta desde P O 1000 con pendiente positiva Quitó el 20% al precio de oferta P O+SUB = (1000*0.8) + (2 X O *0.8) P O+SUB = 800+1.6 X O Tomando el principio de que el Subsidio es igual al Precio del oferente menos el precio del demandante: P O-P D = S Calculó con P O+SUB-P D = S (800+1,6 X)-(8000-5X) 7200-6,6 X = 0 7200=6.6X 7200/6.6 = X 1090.9 = X 1091 = X Teniendo X=1091, reemplazar en las ecuaciones iniciales para obtener el P o y el P D P D =8000-5 * 1091 D = 2545 P O =1000+2 * 1091 O = 3182 Gasto Fiscal = (P O-P D)*X (3182-2545)*1091= 694.967.