Conference Proceedings
MetPlant 2011
Conference Proceedings
MetPlant 2011
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Quantifying Plant Flow Availability for an Alumina Refinery Expansion using Dynamic Simulation
The process design for green- or brown-fields metallurgical plants typically starts with two fundamental elements: the detailed mass and energy balance and the plant flow availability._x000D_
The detailed mass and energy balance is generally developed using process flow-sheet simulation software and represents the steady-state operation of the plant at normal operating conditions._x000D_
The plant flow availability, also known as a range of other industry-specific terms such as flow availability' and operating factor', describes the ratio of plant production to the steady-state mass balance._x000D_
These elements are then used together as the basis for equipment selection and design.Plant flow availability takes into account a myriad of production loss factors including planned and breakdown maintenance, feed availability and composition changes, variability in operational performance and the interaction of batch and continuous unit operations._x000D_
The combined impact of these loss factors are complex and as such plant flow availability is usually derived using a top-down approach such as comparison to an existing operation, a group of similar operations or to an anecdotal industry leading' benchmark._x000D_
These top-down approaches are fraught with risk however, as they cannot factor in changes to equipment selection, flow-sheet or plant operating philosophy, and can result in over-expenditure of scarce capital or shortfalls in plant production.The use of dynamic simulation to provide a bottom-up alternative to quantifying the plant flow availability in metallurgical plant design has been growing rapidly in recent years. It provides a technique to quantify the combined impact of the loss factors described here and a tool to support process design and equipment selection by facilitating a range of sensitivity and what-if analyses._x000D_
FORMAL CITATION:Reynolds, B and Collins, S, 2011. Quantifying plant flow availability for an alumina refinery expansion using dynamic simulation, in Proceedings MetPlant 2011, pp 442-459 (The Australasian Institute of Mining and Metallurgy: Melbourne).
The detailed mass and energy balance is generally developed using process flow-sheet simulation software and represents the steady-state operation of the plant at normal operating conditions._x000D_
The plant flow availability, also known as a range of other industry-specific terms such as flow availability' and operating factor', describes the ratio of plant production to the steady-state mass balance._x000D_
These elements are then used together as the basis for equipment selection and design.Plant flow availability takes into account a myriad of production loss factors including planned and breakdown maintenance, feed availability and composition changes, variability in operational performance and the interaction of batch and continuous unit operations._x000D_
The combined impact of these loss factors are complex and as such plant flow availability is usually derived using a top-down approach such as comparison to an existing operation, a group of similar operations or to an anecdotal industry leading' benchmark._x000D_
These top-down approaches are fraught with risk however, as they cannot factor in changes to equipment selection, flow-sheet or plant operating philosophy, and can result in over-expenditure of scarce capital or shortfalls in plant production.The use of dynamic simulation to provide a bottom-up alternative to quantifying the plant flow availability in metallurgical plant design has been growing rapidly in recent years. It provides a technique to quantify the combined impact of the loss factors described here and a tool to support process design and equipment selection by facilitating a range of sensitivity and what-if analyses._x000D_
FORMAL CITATION:Reynolds, B and Collins, S, 2011. Quantifying plant flow availability for an alumina refinery expansion using dynamic simulation, in Proceedings MetPlant 2011, pp 442-459 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Contributor(s):
B Reynolds, S Collins
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- Published: 2011
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